Teaching an endangered language: situating Irish language teachers’ experiences and motivations within national frameworks of continuing professional development
Bibliographic record
Abstract
Language practices around the world have experienced a significant shift in the last number of years (McDermott, 2011; Walsh, 2005). Communities that continue to speak minority or heritage languages, such as Irish Gaelic, have felt the effects of the various social, political and economic pressures that have gone hand in hand with globalization, resulting in a breakdown in intergenerational transmission (Anderson, 2011; Hornberger, 1998; Norris, 2004). In the Republic of Ireland, the education system has been set as the corner stone of Irish language revitalization efforts since the 1920s, thereby assigning much responsibility to Irish language teachers. Yet, there is a dearth of existing research that gives voice to Irish teachers, and their experiences and motivations to teach a language that just 1.8% of the population speak on a daily basis remain unclear (National Census of Ireland, 2011). In this study, I engage with teachers from both Gaeltacht (where Irish is spoken as a first language) and primarily English speaking parts of Ireland, in order to give a broader account of Irish teachers’ experiences in different educational settings. In addition, I look to identify what implications a better understanding of teacher motivation could have for Continuing Professional Development (CPD) programs offered to Irish teachers, and situate these recommendations within the current educational policies that exist within the Irish education system.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".