Bibliographic record
Abstract
In a world increasingly concerned with security and the problems of how we might perceive one another’s identities, activism in the form of maintaining an open mind has never been more important. Perhaps such an activism begins with an inquiry into knowledge that asks: How do we know about each other? or How do we know about our world? An activism that worries our pat assurances of such knowledge might then proceed to an understanding that most of what we know comes to us through various forms of representational knowledge, be it media, history and other discourses or even our own immediate perceptions. Given that these forms are predicated upon a stable relationship between the person trying to know and the object of understanding, our activism of open-mindedness would then run into the obsessive need for recognition in our world; that is, in the moment of recognition the individual finds a tremendous comfort and reassurance in the self-sameness of identity or in the familiarity of the object. Thus, the forms in which we perceive the world are a problem to an activism of open-mindedness insofar as they are often designed to furnish a sense of security, the comfort of recognition, and a sense of self-sameness between an individual and the world.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.218 | 0.207 |
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".