Bibliographic record
Abstract
At the dawn of a new decade, I cannot help but recall that when I started my academic career in social work in the 1990s, it was common to look ahead to how life would be in the next century. Statistical projections forecast various demographic changes, often using 2020 as the future time frame. Back then, 2020 sounded far away and almost alien. Well folks, the future is here. Now that 2020 has dawned, it seems that the more things change, the more they stay the same. Certainly, the specific issues that social workers address have changed over the decades, and our approaches have been modified to tackle the new issues, but the struggle to understand and meet emerging needs persists. I used to jokingly hear that the ultimate goal of the social work profession was to put ourselves out of business. Given the intransigence of intolerance for difference and the persistent emergence of needs arising from “advances” of modern living, it seems the social justice stance of our profession will never be fully met. Indeed, our social contract is continually expanding. In the Fall 2019 issue of Advances in Social Work we are pleased to present 14 papers--11 empirical, 3 conceptual--written by 29 authors from 12 states across the U.S., representing different regions of the country and Ghana. Each paper is briefly introduced below.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.430 | 0.329 |
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".