Bibliographic record
Abstract
Reflective writing is somewhat like standing naked before the mirror. It is chilly, uncomfortable, a writer’s imperfections raw and exposed. Particularly, Gillie Bolton (2010) says, “through-the-mirror” (p.10) writing takes oneself past the silvered glass right into the roots of emotional thinking – an unfamiliar, perhaps even illogical, terrain. Such writing submitted to a professor, or a cohort, is subject to scrutiny and feedback, comments that may unwittingly jar, poke and provoke, or feel just a little too intimate. However, as Bolton further indicates, “We have full authority over our writing at every stage, including rereading to ourselves and possibly sharing with a confidential trusted reader” (2010, p. 47). After a discussion about ‘digging deeper’, I realized that any ‘holding back’, on my part, was not unconscious denial but was, rather, a conscious decision to maintain a private stance. This poem arose from that acknowledgment.
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 | 0.025 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".