Bibliographic record
Abstract
I will consider dear to me those who have taught me the art of teaching, as well as my peers engaged in the endeavors of teaching, as they are worthy of respect. I will also respect students, as it is an honor to be trusted by them to impart the knowledge they seek. I will seek to constantly improve my skills as a teacher. I am also obligated to teach the art of teaching to those who require this skill in the course of their profession. I will teach those who seek to learn from me according to my ability and judgment. I will strive to keep current on my knowledge of the subject I teach. Never will I presume to teach that which I do not know. I will not use my position as teacher to influence a student towards any purpose but learning. In every situation where I am called upon to teach I will do so only for the good of my students through exercising compassion, aspiring to truth, and keeping myself far from all intentional ill-doing. I will use all reasonable means at my disposal to assist a student's learning. If I am unable, for any reason, to meet the learning needs of a student, I will make every attempt to place that student under the supervision of a teacher more suitable to that student's needs. If I suspect that a student is incapable of learning that which I am trying to teach, I will exercise diligence in confirming this assessment. If it becomes clear that a student is trying to learn something beyond their grasp, I will compassionately cease educating that student and support the student's endeavors elsewhere. I will not use my position as teacher to exploit students for my personal gain. If ever I am unable to follow these teaching tenets, I will remove myself from my position as teacher.
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 | 0.015 |
| 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 | 0.009 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.077 | 0.053 |
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".