The interest of aboriginal landscape imagery in the Tursujuq Park creation (Nunavik, Canada)
Bibliographic record
Abstract
The Tursujuq National Park Project (aka Guillaume Delisle-Lac-à-L'Eau Claire) is now in progress. Beyond the preliminary regulatory studies carried out since 2002 by both the Quebec government and the Inuit institutions, in order to develop a network of national parks, the research focuses on understanding how the analysis of the Tursujuq park landscape perceptions could complement and adjust the park's conception. The perceptions analysis deals with the iconographic landscaping representations and their related practices. The purpose is to create and analyse inuit landscape images of the park's perimeter or vicinity. The fact of choosing a landscape representations vector for the study has made possible the creation of an Inuit Landcape Imagery via a Landcape Photography Contest and a Children's Landscape Drawing Workshop. The framing and composition or the photographs, showing a selection of places and areas as well as particular elements of landscape, will contribute to a better analysis of underlying landscape preferences. These landscape representations will also bring out associated landscape practices. Ultimately, such expressions of landscape imagery are expected to weigh in the decision-making process of the perimeter, zoning and routing of the future Tursujuq National Park.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".