Bibliographic record
Abstract
Field education is a significant component of social work education and might be the signature pedagogy. Yet current issues limit the quality of experience that students can receive from practicum. These issues include austerity policies, exhaustion of altruism, trends towards clinical social work and privatized human services, and a lack of student readiness. Degree granting institutions, such as universities, have long relied on altruism to run social work field education programs but austerity measures may mean that historical altruism can no longer be relied upon. The lack of compensation for practicum students and field instructors is unethical and may contribute to conflicts of interest and exploitation in practicum. This article details these present challenges in social work field education through incorporating scholarship and reflection and presents ideas for change. Enhanced orientation and stipends for practicum students and field instructors have shown preliminary promise in mitigating modern social work field education difficulties. For field education to be social work’s signature pedagogy, practicum planning and associated policies must be treated with respect and rigour, constantly scrutinized and improved upon as the field develops and changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.129 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.085 |
| Scholarly communication | 0.046 | 0.053 |
| Open science | 0.010 | 0.018 |
| Research integrity | 0.023 | 0.031 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".