Bibliographic record
Abstract
Abstract. Based on an ongoing qualitative and collaborative research project led in partnership with the Innu community of Pessamit, this paper brings into focus some specific issues regarding memories recollection and representation in a context of deterritorialization. The Innu First Nation has a specific historical and political context related to resources exploitation. Since their traditional lands have been the site of several large-scale hydroelectric projects, they have been intimately – and to a large extent, forcibly – involved in the economic transformation of Quebec since the 1950s. It should be noted, however, that their ancestral occupation has never been formerly recognized by the federal and provincial governments, a political and legal context partly responsible for the material and cultural losses they had to deal with. Through interviews we have conducted with the elders that travelled the rivers before the floods, we tried to rebuild, in some way, the cultural heritage embedded in those submerged lands. We used different cartographic tools and materials in a way to support and trigger the personal narratives the elders were remembering and sharing. This cultural mapping process revealed three main issues I would like to focus on. First, as the cartographic representations were getting closer to the landscapes the elders perceived and experimented as kids and young adults, the localization of significant places and the creation of personal narratives became easier and fluid. Secondly, we found, through that inquiry, how important an enhanced visibility of innu’s flooded heritage can be on a political level. Finally, we came to the conclusion that mapping should be considered more as a conversation than a visual representation only.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".