Bibliographic record
Abstract
The term Curriculum Vitae (CV) is a Latin expression which literally translates to course of one's life. It is used to refer to an outline of someone's educational and professional history, prepared and submitted (particularly but not only) for employment purpose (job application). Another name often given to CV is resume. However, in countries like Canada (particularly in Quebec) and USA, a distinction is made between the two terms (CV and resume). The CV is most often used for academic positions and is conceived to be longer than the resume, as it includes information on publications, conferences attended by the candidate and the like. A resume, on the other hand, is conceived to be shorter and to exclusively contain information that may be relevant to a particular position. The resume's content needs to capture a number of key areas including (i) a personal profile statement, (ii) role undertaken, (iii) skills and abilities, (iv) educational qualification and ongoing personal development and (v) hobbies and interest (Brisk 2011: 6; O'Brien 2000: 123). In this chapter, we shall use CV as a general term.
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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 | 0.037 | 0.270 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.211 | 0.161 |
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".