AFSCME's <i>Social Worker Overload</i> : Digital media stories, union advocacy and neoliberalism
Bibliographic record
Abstract
This article presents a case study analysis of Social Worker Overload, a digital media story created by the American Federation of State, County and Municipal Employees (AFSCME) and shared publicly using the social media site YouTube. This story uses worker testimonials to present a compelling story about the effects of neoliberalism on social care work in the field of child protection. This story illustrates how the Department of Children and Family Services (DCFS) in Washington State uses ‘evidence based practice’ discourses to limit the forms of knowledge that may be utilized in discussions of work overload and work design within the child protection system. Through the creation and sharing of a digital media story about their experiences, the workers present narratives demonstrating how these and other elements of neoliberalism limit the workers’ capacity to actualize the potential benefits of professional social work. Finally, the analysis considers the process of worker advocacy using digital media practices, highlighting the roll that unions play in facilitating this type of resistance.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.034 | 0.027 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".