Professional autonomy as a criterion for classification of social work tasks in a child welfare setting
Bibliographic record
Abstract
This study developed from results obtained in a recent MSW thesis entitled, "Utilization of Manpower at Children’s Aid Society of Vancouver, B.C." by Adams, et. al. (U.B.C. School of Social Work, 1967). In the main, our assignment was to select and rank a wide range of tasks performed by agency staff in the field of child welfare. Using the Adams et. al. recommendation regarding "worker autonomy," we selected a panel of fifteen judges representing the three levels of employment - administrative, supervisory, and line worker, developed an adequate method of judging, and analysed the data. The Adams, et. al. study proved useful in our inquiry in providing clues to the various personal assignments we had set for our project group. The entire project covered a period of less than three months and because of this comparatively short research period, we resorted to simplified techniques of judging. We found a high percentage of agreement among the judges. This not only indicates that the tasks can be differentiated by social workers in the field of child welfare but also that the "forced choice" phase of the judging probably does not adversely affect reliability. Further, we suggested in the Adam's study, it would appear that "worker autonomy” can usefully be used as a criterion in ranking tasks in a child welfare agency.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".