Conclusion: language and power <i>à la</i> Jim Cummins
Bibliographic record
Abstract
The conclusion begins with an analysis of the common thread that ties the papers included in this special issue together. The collection hinges on an analysis of issues of language and power in diverse contexts, seen in a perspective à la Jim Cummins. Included in the Conclusion is a discussion of how the papers in this collection illustrate or operationalize Cummins' (2001a) empowerment framework. The conclusion also discusses the diverse ways in which the papers examine the influence of societal power relations on educational structures and classroom instruction. Next, the explanatory value of Jim Cummins’ empowerment framework is put to the test by evaluating its ability to account for the wide range of contexts of issues of language and power in the various texts. This is followed by a summary of key lessons learned from what the authors identified as constraints limiting bi/multilingual development in their contexts, followed by an analysis of overarching themes emerging from those constraints. Finally, current options for dealing with the constraints identified are reviewed, and recommendations are made as to promising paths of future research in the study of language and power.
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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".