Dr. Anne L. Jefferson Support of Elementary and Secondary Education for the “Regular ” Student Population: Some Concerns
Bibliographic record
Abstract
Support of elementary and secondary education for all is the operating premise for most governments. The exceptions are under constant external pressure to conform. This stance of inclusiveness is one we can say is the accepted norm. With this norm various expectations have developed as to what education looks likes and how it should be supported. Unfortunately, the what and the how have not easily coexisted. The purpose of this paper is to discuss a growing dilemma between the what and the how. The importance of lobby groups skewing the concept of “equity ” so that “adequacy ” of funds has less of a chance with respect to the regular student population is used to illustrate the dilemma. Over the years, the recognition of the existence of unequal education opportunity for children has shaped how available resources were allocated by government. The allocation plans moved from simple provision of equal resources, based on head counts, to elaborate allocation plans acknowledging variations in local ability to support a local school system. This attention to what is commonly referred to as taxpayer equity in the literature was a significant movement in balancing what education looked like (i.e., a child being able to access an education at a public school) and how it needed and could be supported with available public funds. Over the years, the provincial governments of Canada have done a good job. Consequently, children in Canada do not have to worry whether
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".