Addressing Joshua Fishman’s Ideological Clarification: Working With Pre-service Teachers
Bibliographic record
Abstract
The drastic and tragic loss of Indigenous languages in Canada is of grave concern, as about half of the approximately 50 languages are either endangered or close to extinction (Canada, 2002). Reversing language shift ([RLS) (Fishman, 2001) involves many considerations, including the value and social status of language in homes and communities. This paper explains how a found poetry exercise that 52 pre-service teachers completed has implications for RLS through a movement towards ideological clarification. The poems demonstrate the essence of both Indigenous identity and language, and the classroom-teacher-to-be impressions of that, in rather moving and powerful expressions. These found poems not only speak to the profound loss of languages, but also demonstrate how the activity itself promoted both a personal and a collective understanding of what language obsolescence means to them and their culture. Potentially, they can work as well towards activating goodwill towards Indigenous languages among those who read and reflect on them \n---- \nLa perte drastique et tragique des langues autochtones au Canada est profondément préoccupante, car environ la moitié des quelque 50 langues sont en danger ou en voie de disparition. Inverser le changement de langue (Reversing language shift, RLS; Fishman, 2001) demande de considérer plusieurs facteurs, y compris la valeur et le statut social de la langue dans les foyers et dans les communautés. Cet article décrit comment un exercice de « poésie trouvée » complété par 52 enseignants pré-emplois peut avoir des implications pour le RLS au moyen d’un mouvement vers une clarification idéologique. Les poèmes exhibent l’essence même de l’identité et de la langue autochtone pour les futurs enseignants, de façon émouvante et puissante. Ces poèmes trouvés parlent non seulement de la perte profonde de la langue, mais démontrent également comment l’activité elle-même a promu une compréhension à la fois personnelle et collective de ce que l’obsolescence de langue signifie, pour eux et pour leur culture. Potentiellement, ils pourront d’ailleurs travailler pour l’activation de la bonne volonté envers les langues autochtones parmi ceux qui liront et réfléchiront là-dessus.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.037 | 0.023 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".