Bibliographic record
Abstract
When asked to find a visual expression of my writing process for a first year PhD writing class, I saw a chance to unblock whatever was making it difficult for me to write. Searching for a meaningful way into my story, my ideas were reflected back through images of eyes – the eyes of strangers, my own eyes, and finally through the eyes of those who cared about me. Four years after a Mild Traumatic Brain Injury impacted my life, I returned to pursue an academic career. Symptoms that I thought had been put to rest were once again haunting me and my frustration level was escalating. Trying to find my way back into an academic existence was not an easy journey. The visual inquiry into eyes became a door through which I was able to gain back my words. Using poetic and narrative inquiry allowed for a further opening of releasing obstructions.
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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.029 | 0.015 |
| Scholarly communication | 0.024 | 0.014 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".