Re-envisioning Inclusive Spaces Through the Theory of Positive Disintegration
Bibliographic record
Abstract
Isn’t calling something a positive disintegration a bit of an oxymoron? How can this anxiety, depression, and nervousness that has been dogging my every step as I navigate academia possibly be considered positive when it feels like I am a prime candidate for a bipolar diagnosis? If this feels familiar, please attend my poster session where I will share a graphic representation of Kazimierz Dabrowski’s (1902-1980) theory of positive disintegration (TPD) in a way clear enough to be understood by the high school students I will be inviting to participate in my research. TPD is a theory of personality development that explores the role of emotions in facilitating an individual’s journey toward a self-selected, autonomous personality ideal that is informed by an evolving hierarchy of values that may differ from the environment in which one finds themselves. Introducing TPD into a discussion on inclusion offers a new understanding of students whom Dabrowski (1964) said are often viewed as “unsocial, queer, unadapted, and difficult” (p. 62). As inclusion works toward bringing down barriers that have kept students from being fully integrated into classrooms, a new understanding of emotions within those spaces is an area of research in need of attention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.097 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".