Bibliographic record
Abstract
The work is in a documentary film format utilizing digital video through computer software that presents the interviewees' responses to two questions juxtaposed with images and statements of "a good teacher" from popular culture. The two questions are: 1. Can you recall a primary or elementary teacher whom you had that you thought was a good teacher? 2. What was it about her/him that made you think she/he was a good teacher? However, in some of the interviews the interviewer utilizes other questions and/or prompts in order to encourage the participant to expand or elaborate within her/his story. Like TRINH T. Minh-ha (1992) "storytelling is an ongoing field of exploration in all of my works" (p.144). Through the interviews interpretive themes emerge around the notion of who is/what makes a good teacher. Some of the themes suggest personal characteristics such as "kindness," "patient," "passionate," "calm," "respect," "firm," "understanding" and "encouraging" that entwine with an idea of "personal connection" with the teacher. Other themes suggest shared experience and teaching attributes. These themes are presented with the images of good teachers from popular films. The notion of "good" and "good teacher" are purposefully not demarcated or defined by the interviewer or asked of the participants. This term is implicitly defined in the stories shared by each of the participants and the stories of the teacher films. URN: urn:nbn:de:0114-fqs0802415
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.079 | 0.040 |
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".