Na cineálacha tacaíochtaí a bhíonn ag teastáil ó pháistí le huathachas, dar le múinteoirí, chun ionchuimsitheacht rathúil sa suíomh bunscoile lán-Ghaeilge a chinntiú
Bibliographic record
Abstract
Is í aidhm an taighde seo ná chun tuairimí múinteoirí i leith na cineálacha tacaíochtaí a bhíonn \nag teastáil ó pháistí le hUathachas chun ionchuimsitheacht rathúil sa suíomh bunscoile lán- \nGhaeilge a chinntiú. Rinneadh agallaimh le ceathrar múinteoirí ó bhunscoileanna Béarla agus \nceathrar múinteoirí ó bhunscoileanna lán-Ghaeilge chun comparáid a dhéanamh idir na \nsuíomhanna éagsúla maidir le páistí le huathachas a thacú agus ionchuimsitheacht a chur i \nbhfeidhm. \nIs iad na torthaí a tháinig as an taighde seo ná go bhfuil géargá ann níos mó acmhainní a chur \nar fáil as Gaeilge do mhúinteoirí agus do dhaltaí. Míníodh na tacaíochtaí a bhíonn ag teastáil ó \npháistí le hUathachas maidir le riachtanais céadfacha, amharcacha, scileanna sóisialta agus \nmothúchánacha. Léirigh na múinteoirí nach bhfuil siad muiníneach as páistí le hUathachas a \nthacú, go háirithe sa suíomh bunscoile lán-Ghaeilge agus mhaígh na múinteoirí go bhfuil níos \nmó cúrsaí don bhforbairt ghairmiúil leanúnach ag teastáil. Áfach, bhí gach múinteoir \nmuiníneach as ionchuimsitheacht a chur chun cinn sa seomra ranga. Cé go mbíonn \nionchuimsitheacht i bhfeidhm, is léir go bhfuil níos mó traenála agus tacaíochtaí ag teastáil ó \nmhúinteoirí sna suíomhanna bunscoile lán-Ghaeilge chun an tacaíocht is éifeachtaí a chur ar \nfáil do pháistí le hUathachas. Fágann sé sin le tuiscint go bhfuil níos mó taighde ag teastáil \nchun leibhéal ionchuimsitheacht na bpáistí le hUathachas a fhiosrú agus chun na tacaíochtaí \ncuí a chur i bhfeidhm.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.010 |
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".