Chapter 1. On Poverty and Advocacy: Submission to the 1983 CCSDTask Force on Poverty
Bibliographic record
Abstract
The rediscovery of poverty in the late sixties led to a proliferation of well-intentioned studies on Canadian poverty. Some fifteen years later the problem of poverty has not been solved. Nor are we any closer to a solution, even though there are now more data on the poor than can ever by analyzed (Hofley, 1980). It is this reality that raises the issue of the usefulness of further poverty research. One is indeed hard pressed to find evidence which suggests that another Task Force on poverty is the right way to go about helping the poor. Another fact finding mission, of the type outlined in the terms of reference of the Task Force (SPAAN, 1983), will certainly result in more information on the poor. It may, however, be just as instructive to reflect on the potential benefits of the type of data produced in such inquiries. What have we learned? Where has this research taken us? And, can we expect to shed additional light on the nature of Canadian poverty by asking questions that have been repeatedly investigated over the last one and a half decades? Certainly we are now more informed. Research has documented the scope of poverty and has given us a glimpse into the magnitude of suffering that is engendered by
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.033 | 0.018 |
| Insufficient payload (model declined to judge) | 0.028 | 0.016 |
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".