A response to Dr Michael Oakes: advancing research into SES mechanisms that affect health
Bibliographic record
Abstract
We read with interest the commentary of our recent paper1 by Dr Michael Oakes.2 While we agree with many of the points raised by Dr Oakes, we would like to clarify a few points where we feel our work may have been misrepresented. We agree that more information concerning our main independent variable would be of use. Unfortunately, we do not have access to detailed information on the university attended by each respondent. We take Dr Oakes’ point about differing educational institutional quality conferring different probabilities of finding employment (although we strongly suspect that differences in perceived education quality across Canada’s universities are much smaller than those in the US!). Further, we would expect social class to be related to both educational institution quality, and the probabilities of finding employment, via both perceived educational quality, and personal or parental social connectedness. There is a long list of questions we would have liked to have been able to analyse, but were not asked of respondents to the National Population Health Survey. For example, it would have also been useful to know if each respondent’s occupation was related to his/her education (or as Dr Oakes suggests, if their occupation meets their educational aspirations). However, in each case we feel we would have incorrectly classified respondents as overqualified, who did, in actual fact, not feel overqualified, or did not expect higher occupational attainment as a result of their studies. As such, we can only see this misclassification as biasing our results downwards, reducing the effect we have found in university-qualified respondents.
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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 | 0.024 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.046 | 0.096 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".