The use of Facebook in the recruitment of foster carers: A dialogic analysis
Bibliographic record
Abstract
Social media is becoming increasingly important for communication and community building, yet research on the use of social media by non‐profit organisations is limited and largely restricted to content analysis of social media comments. This article contributes to addressing this research gap, through a survey‐based study of the perspectives of key informants in U.K. Local Authority fostering teams on their use of social media. Specifically, it examines the extent to which the Facebook activity of local authority fostering teams is aligned with the principles of successful social media engagement, as represented by dialogic strategies and outcomes. A questionnaire on the use of Facebook was circulated to all local authority fostering teams in England. Findings suggest that although there is progress, many teams are at an early stage in their social media journey and that there is considerable variation between agencies. The limited evidence of engagement in relation to dialogic principles suggests that there is some adoption of a strategic approach. In particular, of the three dialogic principles associated with successful online engagement, two (updating and community building) were applied by about half of local authority fostering teams and the third (engagement) by just over a quarter.
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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 | 0.034 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".