Quantifying Abuse
Dear Humanity…
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This report demanded every part of me to write, yet I believe every ounce of that effort will be worth it.
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Today, we stand at the threshold of change.
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Today, we commit to transforming the world in which our children will inherit.
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Today, we break the curses that have bound us for generations.
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Together, we begin a new chapter of hope.
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Within these pages lies a quantitative analysis of the abuse I endured following my separation from my partner during 2023–2024. This work introduces over 20 metrics, establishing an objective framework to distinguish between abuser and victim. Its purpose is not persecution, but prevention – mitigating risk and, above all, providing help.
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We must end institutional violence.
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We must restore hope to the thousands who lose years of their life in courtrooms, fighting only for peace.
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Consistent metrics means consistent judgements in our courts. They mean fairness – where, for far too long, there has been none. This system will compel abusers to take notice. They will try to test the boundaries, but those boundaries will be clear. They will have to start paying attention to every word they speak.
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Let me repeat: this system will force abusers to pay attention to what they say. And in doing so, we raise consciousness.
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We fix this problem today. And today, we move forward.
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With all my light,
Ash
Contents
Within this report, I present a two-level analytical framework for abuse. The first level (trifecta) provides a clear, pragmatic approach that can be implemented immediately. Its parameters can be automated with current Artificial Intelligence (AI) technology and, where needed, replicated manually – ensuring access even without technological support. Each section also includes an examination of data categorization for the abuse type.
The second level examines the vehicles of abuse which are subjective indicators which require advance data recognition and may be considered for future inclusion.
This Framework must be both comprehensive and adaptive. In practice, adversarial actors will probe for gaps; our framework anticipates this by minimizing ambiguity and iterating based on misuse.
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The first-level of analysis is concise and consistent, enabling fair, equal application. The second-level analysis, as documented here, is subjective indicators and open to interpretation. It was conducted manually and includes overlapping data layers. Going forward, data experts should determine which classification, thresholds, and validation procedures are required before formal integration.
Emotional Abuse Structure:
Despite months of research, I was unable to locate a comprehensive, standardized, and recognized framework to categorize types of emotional abuse. Existing resources offered fragmented lists of toxic behaviours but no unified structure. While numerous lists exist, often published on psychologists’ websites and online resources, they are inconsistent and lack cohesion.
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The structure I ultimately developed to analyze my own experience of abuse – Demeaning, Blaming, and Threatening – was not immediately apparent. Like many of my data projects, it required hundreds of hours of detailed work: examining communications, breaking them apart, and identifying recurring patterns. Over time, this structure revealed itself as comprehensive, encompassing every abusive element I was able to extract from the dataset.
The Trifecta of Abuse
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Within this structure lies what I call The Trifecta of Abuse: Demeaning, Blaming, and Threatening. These three actions are consistently used with the intent to harm, manipulate, and control the recipient.
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Demeaning is a direct attack on a person’s character or sense of self.
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Blaming assigns fault or responsibility for something that has already occurred in the past.
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Threatening projects fault or responsibility into the future, warning of harm or negative consequences that may occur.
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Each of these actions is further sub‑divided for clarity. Demeaning incidents are classified by type; while Blaming and Threatening are categorized by subjects.
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In addition to these core categories, the report also analyzes the vehicles of abuse — the methods through which abusive actions are delivered. These include Harassment (or “bombing”), Projection/Gaslighting, and Coercive Control/Triangulation.
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The level of detail provided ensures that readers — and future victims who may rely on this structure — can clearly assess their own experiences. The system is deliberately simple, making it accessible across socio‑economic contexts and usable with or without modern technology.
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Benefits of the Classification System
This structure is comprehensive. Every abusive communication identified in the dataset could be categorized within these three groups. No incidents were left unclassified or proved too ambiguous to fit. This consistency demonstrates the robustness of the structure.
Polarization of Abuse Types
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In my case, the distribution of abuse is heavily polarized toward Blame. Future research should explore whether this polarization is unique to domestic abuse contexts, or whether other environments — such as bullying or workplace harassment — show stronger tendencies toward Demeaning or Threatening behaviors. Understanding these patterns will help refine the structure and improve its applicability across different forms of abuse.
DEMEANING
Definition
“Demeaning behaviour is any action or communication that makes someone else feel inferior or less valuable than they actually are…. This behaviour comes in many different forms. Expressions of demeaning behaviour may include verbal, non-verbal, and overt behaviours.”[1]
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At its core, demeaning behaviour undermines a person’s sense of self. Because self‑concept varies from individual to individual, what is experienced as demeaning will depend heavily on the victim’s identity, values, and self‑worth. Abusers exploit this variability, tailoring their attacks to the vulnerabilities they have learned about the victim.
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Scope and Synonyms
This category is broad, encompassing behaviours such as belittling, insulting, and shaming. From the abuser’s perspective, demeaning is often used as a competitive tactic—an attempt to erode the victim’s confidence and establish dominance.
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[1][1] Demeaning Behaviour And How To Respond. Demeaning Definition And How To Recognise This Behaviour
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Abuse is possible in part because the Victims self concept is pliable.
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i.e. – if someone call them ‘stupid’ and their self concept is in a state wherein it can change, they may reflect and ask themselves, “am I stupid?”, and their self concept changes.
This is not due to fault, but due to conditioning. Conditioning that can be repaired.
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Victims should work to repair their self concept. Love yourself so much you have extra to give away.
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Demeaning Data Categorization
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For the purposes of this report, demeaning commentary has been divided into three subcategories:
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Direct Insults – Explicit statements that attack a specific character trait or aspect of the victim’s personality.
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Insinuated Traits – Indirect or implied criticisms that suggest flaws in the victim’s character without stating them outright.
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Malice – Broad, hostile attacks on the person as a whole, often expressed through “down‑talking” or dismissive language.
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In addition, a distinct pattern of Forcing Gratitude has been identified. In these instances, the abuser degrades the victim by demanding expressions of gratitude, thereby reinforcing a dynamic of control and humiliation.
BLAMING
Definition
“Blame is simply the discharging of discomfort and pain. It has an inverse relationship with accountability.” – Brené Brown
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In the context of abuse, blame occurs when the abuser assigns accountability for a fault or wrong to the victim. This act is abusive because the responsibility is imposed rather than earned. On its own, blame may appear unreliable as a metric of abuse; however, when analyzed alongside Demeaning and Threatening behaviours, it becomes a critical component of the dataset.
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Structural Features
Blame can be identified in communication by its reliance on past events. The abuser references something that has already occurred and frames it as the victim’s fault. For blame to be effective, both abuser and victim must share the belief that the event carries negative weight. This dynamic reinforces guilt and undermines the victim’s sense of agency.
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Repeated incidents of blame often cluster around specific subjects or events (e.g., the “Hackcident” or “Blackura”). These recurring references provide valuable analytical data, offering insight into the narrative of abuse without requiring unreliable eyewitness testimony.
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It is important to note that while blame is consistent and measurable, the data itself does not prove the legitimacy of the incident. Instead, it provides a structured, data‑based viewpoint that can inform interpretation.
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Blaming Data Categorization
For this report, blame incidents were manually categorized by subject. This process is inherently subjective, with overlapping categories. Future AI‑driven analysis could improve consistency through matrix‑level tagging systems, but this complexity was beyond the scope of the current study.
Malicious Subcategories
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Two forms of blame are inherently abusive because they cannot be resolved and serve only to manipulate:
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Behaviour (“Always/Never” statements):
The victim is accused of inherent flaws in their behaviour. Because the blame is vague and unresolvable, the victim is left with a constant sense of wrongdoing and no path to correction.
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Reaction:
The victim is blamed for the abuser’s emotional or behavioural response. This tactic is a clear sign of manipulation, shifting responsibility for the abuser’s actions onto the victim.
THREATENING
Definition
A threat is a statement that signals potential harm in the future. Its purpose is not merely to warn but to control behaviour — placing the victim in a position where decisions are shaped by fear of negative consequences. In this way, all threats function as a form of coercive control, compelling the victim to act in ways they would not otherwise choose.
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Structural Features
Threats are the inverse of blame. While blame anchors itself in past events, threats are rooted in the anticipation of future harm. They can often be identified by conditional structures: “If you do X, then Y will happen.”
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The severity of threats varies widely depending on context and perceived weight. Some threats resemble bargaining, but with the addition of a punitive consequence. Others are vague or indirect, leaving the victim to imagine the worst. This ambiguity magnifies their impact, as the victim must fill in the blanks with fear.
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It is important to note that not all threats are explicit. Many are insinuated, with harm suggested rather than stated outright. While this dataset does not measure severity, future analytical frameworks should incorporate weighting systems to capture the full impact of these variations.
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Reliability
Among the trifecta of abuse categories, threats are often the most reliable for determining fault. Unlike demeaning or blaming, threats already have established recognition within legal systems, making them easier to codify and apply in judicial contexts.
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Threats Data Categorization
As with blame, threats were manually categorized by subject. This process is subjective and overlapping, but it provides useful insight into recurring patterns. Future AI‑driven matrix tagging could improve consistency and reduce ambiguity.
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A notable subcategory involves unspecified threats—statements such as “Something bad will happen” without clarifying what that harm might be. These are particularly disarming, as the victim is left to imagine the scope and nature of the abuse to come.
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For clarity, these have been divided into two groups:
Insinuated Threats: General warnings that harm may occur.
Insinuated Abuse: Warnings that harm will occur and the victim will suffer.
This distinction highlights how even vague language can exert powerful coercive control, destabilizing the victim’s sense of safety and autonomy.
VEHICLES OF ABUSE
Like the core types of abuse — Demeaning, Blaming, and Threatening — the vehicles of abuse also overlap and interact, forming a secondary trifecta. These vehicles describe how abusive behaviours are delivered rather than what they are. Because of the complexity involved in tracking them comprehensively, analysis at this level is beyond the scope of the current report. Future development should explore matrix‑level analysis to capture these overlapping dynamics more effectively.
HARASSMENT & BOMBING
Definition
The term harassment is defined inconsistently across sources, sometimes broadly enough to encompass nearly all abusive communication. For the purposes of this report, harassment is defined more narrowly as unwanted communications. These can be measured either by the sheer volume of words exchanged or by the frequency of abusive incidents identified through the trifecta metrics.
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Significance in This Dataset
Harassment is particularly relevant here because clear boundaries were established at the onset of the analytical period, yet repeatedly violated. Future analysis should consider whether boundary violations can be tracked directly in data, elevating incidents where limits are explicitly crossed. This would constitute a second‑level analytical item.
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Application to Legal Processes
The metrics developed in this section could be readily applied to restraining order procedures. At present, restraining orders are case‑specific and require judicial discretion. Introducing consistent, quantifiable harassment metrics would represent a logical evolution, improving systemic efficiency and reducing costs.
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Escalation Tracking
Harassment metrics also provide a straightforward way to monitor escalation in high‑conflict cases. With AI‑enabled tracking, real‑time monitoring could identify rising patterns of abuse and allow designated authorities (outside of traditional policing) to intervene before tragedy occurs.
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NEW CONCEPT: BOMBING = HARASSMENT + NON-VERBAL ABUSE
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In addition to the influx of communications, there was also a number of what is being defined as “Non-Verbal” identified within the Blame section of the data. While these may seem insignificant independently (changed accounts, invasions of privacy, missing items), when coupled with harassment metrics becomes extremely destabilizing for the victim. This is a form of torture.
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Future analysis of this should include an escalation component as this is how it is impactful on your neurological health. This data could be accessed by cross examining the harassment metrics and Blame
PROJECTION & GASLIGHTING
Definition
Projection and gaslighting are distinct but structurally connected forms of abuse. Both involve the manipulation of reality for the abuser’s benefit:
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Projection is the act of displacing one’s own feelings, flaws, or motives onto another person. The abuser creates a distorted version of reality and attributes it to the victim.
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Gaslighting is the corresponding abuse inflicted on the victim, who is pressured to accept and internalize this false reality.
In practice, projection is the action, while gaslighting is the impact. For example: the abuser projects a new reality; the victim is gaslit into believing that reality exists.
Dual Perspectives
Projection can occur on both sides of a conflict, though it is labeled differently depending on perspective. From the abuser’s side, projection is a manipulative tactic. From the victim’s side, it may manifest as “rose‑colored glasses”—a distorted perception that minimizes harm. Because both realities are skewed, neither party’s testimony is fully reliable. This underscores the importance of structured data analysis to reveal patterns that subjective accounts cannot.
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Measurement and Analysis
Projection and gaslighting can be tracked in tandem with the Trifecta of Abuse metrics (Demeaning, Blaming, Threatening). Comparable data points allow for cross‑validation. For instance:
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When an abuser says, “I’m not threatening you,” but the dataset clearly contains threatening language, this contradiction is evidence of gaslighting.
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Without the trifecta metrics, such statements could be misinterpreted as blame or dismissed as subjective.
Accurate measurement requires cross‑analysis or carefully programmed AI systems to distinguish between overlapping categories and ensure reliability.
COERCIVE CONTROL & TRIANGULATION
Coercive Control
(reference) “Domestic abuse isn’t always physical. Coercive control is an act or a pattern of acts of assault, threats, humiliation and intimidation or other abuse that is used to harm, punish, or frighten their victim”
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Definition
Coercive control refers to a pattern of behaviours designed to dominate, intimidate, or restrict the victim’s autonomy. It is not always physical; rather, it encompasses threats, humiliation, manipulation, and other tactics that place the victim in a position where they must make decisions they would not otherwise choose.
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Triangulation is a specific form of coercive control in which the abuser introduces a third party into the dynamic. This third party is used as a tool to divide, manipulate, or exert pressure on the victim, often creating isolation and reinforcing dependency on the abuser.
Legislative Context
Coercive control has recently become a focal point in legislative developments across multiple countries. However, existing definitions are often vague, describing only “patterns of behaviour” without specifying what those behaviours entail. This lack of clarity raises concerns about consistency and fairness in application, leaving room for subjective interpretation and potential misuse.​
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​Suggested Definition
Coercive control is the purposeful act of placing another person in a position where they must make a decision they would not otherwise make.
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Proposed Revision
To strengthen its utility, coercive control should be defined more comprehensively. A clearer definition would encompass behaviours both within and beyond domestic abuse contexts, ensuring that the term captures the full spectrum of manipulative tactics.
