Bibliographic record
Abstract
The Laughing People , translated from the award-winning Le peuple rieur, conveys the richness and resilience of the Innu while reminding us of the forces – old and new – that threaten their community. This memoir and tribute tells the tale of the very long journey of a very small nation, recounting both its joie de vivre and its crosses borne. Readers follow Serge Bouchard, a young anthropologist in the 1970s, as he arrives in Ekuanitshit (Mingan, Quebec) and comes to know its residents. His observations and questions document a community weathering yet another season of change – skidoos replace dogsleds and forests are bulldozed for prefabricated housing – while nonetheless defying external pressures to assimilate or disappear altogether. Returning to these texts fifty years later, Bouchard moves beyond platitudes of strength and dives into wide-scale injustices to present the sacrifices and beauty of the Innu people on individual terms. Whether recounting the impact of the residential school system on Georges Mestokosho, the wave of Innu activism inspired by An Antane Kapesh, or the uncelebrated work of women like Nishapet Enim, The Laughing People presents an opportunity for readers to be part of the preservation and proliferation of these important stories.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.016 |
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".