Sites and Shapes of Transinstitutionalization
Bibliographic record
Abstract
The study of transinstitutionalization necessarily varies by context. In this issue we guard against misconceptions that institutionalization is an action that took place in the past, whose loose ends we are now trying to tie together and where contemporary institutionalizing conditions are merely legacies that will, in time, fade away. To think of institutionalization as something of the past is to gently scratch its surface. And, given the wide breadth of transinstitutionalization and the many lives and stories it encompasses, we are aware of the limitations of covering this vast topic in one special issue. Yet, following a call to include disability in developing new approaches to understanding modernity (Van Trigt, 2019), our aim with this collection is to gather the latest research and reflections on transinstitutionalization as a topic that can take flight in our theoretical and cultural imaginations, a topic that can help us transcend the dangers of “theoretical complacency” that come with imagining the ongoing as a past, one-time thing (Bauman, 2000, p. 3).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.084 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".