Bibliographic record
Abstract
This chapter focuses on social work practice approaches to the assessment of communities. Specifically, this chapter presents and examines the theoretical foundations that underpin, shape, and direct the concepts of community, community social practice, and the assessment approaches of this field of practice. It discusses the historical background, context, and emerging issues relating to assessment in community social work practice. In addition, it examines the evidence base of the current approaches to community assessment and intervention. Finally, it discusses the implications of the current state of this knowledge for micro, mezzo and macro practice and presents some conclusions that will suggest direction for integrating technology policy, practice, and research. The chapter demonstrates that approaches to community assessment are evolving to be focused on more dynamic iterative processes which are inextricably connected to the intervention process. It has argued that community practitioners need to become allies with the community residents in mobilizing, engaging, and building the capacity of community members as early as possible in the assessment process. Finally, it indicates that the rapid growth in communication and information technology is being incorporated into the assessment process as a means of extending the social inclusion of marginalized minority groups.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".