Governance and Funding of Voluntary Secondary Schools in Ireland
Bibliographic record
Abstract
It is important to note that the Department offers training possibilities for the members of BOMs -for further information, see: http://www.education.ie/en/Schools-Colleges/Information/Boards-of-Management/Board-of- Management-Training.html.The scheme was introduced in recognition of the responsibilities of Boards of Management outlined in the Education Act and in consideration of the increasingly complex environment in which they must operate.10 While there may also be differences within the three sectors, this study focuses on differences across the three types of second-level schools.11 In addition, disparity of funding can be observed in other areas: Chaplains are funded by the state for VEC and community/comprehensive schools (amounting to an estimated €9 million per annum) but not for voluntary secondary schools.Letters containing a detailed description of the aims of the study and the procedures involved were sent to the prospective interviewees.In total, 23 key stakeholders were selected and interviewed, mostly on a one-to-one basis (with some in a small group) (see Table 1.1).Where the person initially identified was not available for an interview, another individual from the same organisation was selected.The interviewees represented the voluntary secondary, vocational and community/comprehensive sectors and other organisations, such as the Department of Education and the Education Commission of the Irish Catholic Episcopal Conference.TABLE 1.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".