Lessons from abroad: Rebalancing accountability and pedagogy in the Irish social care sector through the use of effective leadership
Bibliographic record
Abstract
Abstract A concern is emerging in Ireland that social care managers and staff are moving too far away from the ‘care’ in social ‘care’ work. In this paper a discussion of the impact of the bureaucratic procedures and regulation within the social work and social care work sectors is presented along with an exploration of leadership approaches. It is argued that certain leadership approaches, in particular pedagogical leadership, could not only help social care managers to negotiate the complex issues they are facing but also facilitate putting the ‘care’ back into social ‘care’ work. Pedagogical leadership is globally supported across a variety of human service disciplines: it facilitates the creation of a learning culture within the workplace where social care managers facilitate conversations with their teams to encourage reflection, critical thinking and contributions to the professional wisdom required for quality service. The purpose of this article is to contribute to the dialogue within leadership practice for social care professionals. This discourse is necessary if lessons are to be learned from past experiences in this country and others about how to balance the need for care, learning and compassion with accountability.
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 | 0.024 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".