Innovative Family Mediation Research Initiative Embedded in the Community In Ireland
Bibliographic record
Abstract
The Family Mediation Project is a not‐for‐profit means‐tested research initiative at Waterford Institute of Technology, based on an innovative family mediation model developed by Dr Roisin O’ Shea, following her Irish Research Council funded doctoral research. The project, led by W.I.T.’s Dr Sinéad Conneely (coordinator) and Dr Roisin O’ Shea (principal investigator), is test‐running the next iteration in family mediation, embedded in the community, comprising of the most effective elements sourced globally, with a particular focus on innovations in Canada, and is gathering empirical data to evidence outcomes. The final “real world” phase of the project commenced in May 2018, an exciting collaboration between voluntary, statutory agencies and a research institution to further test the effectiveness of this innovative approach on a larger scale at community level in the south Dublin area. This paper will discuss the project innovations and efficacy of the projects objectives, to provide effective mediation as quickly as possible for families and their children, within their community, by experienced family mediators, with hook‐ups and sign‐posting to trusted existing resources, such as the support services offered by the Family Resource Centres, and on‐line and face‐to‐face resources, with the court‐room as an end of pipe‐line solution or emergency forum only.
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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 | 0.035 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".