Bibliographic record
Abstract
Over the years leading up to my enrollment into graduate school, my process of film and video capture, what I called "filmmaking," consisted of largely improvisational experiments with my camera and a group of actors.I continued this method of making all the way up to my fifth quarter review, at which a radical shift in my making occurred.It was suggested to me by my committee that I change the way I think about my work, which resulted in a whole new way of making my work.This new process involved pre-production, as well as taking the proper time to edit my footage, particularly in the stage of studying the raw clips to allow them to reveal what they are to me.This shift caused me to drastically change the way I look at my work.I abandoned trying to force the work into what I thought it was, and have begun looking at what the work really is.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.082 | 0.023 |
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".