Cartographie critique de réalités géographiques : cas de la planification de l’espace marin: Analyse comparée franco-canadienne
Bibliographic record
Abstract
The Marine Spatial Planning (MSP) is the new device from modern states looking to manage marine space within their maritime jurisdictions. MSP relies on the knowledge of some scientific disciplines and the power of geo-technologies (ex. GIS) to inform and map the existing conditions of the sea. Like numerous other normative management frameworks of geographic space, this device uses the acquisition, the treatment and the cartographic representation of the data pertaining to the human (activities) and non-human (ecosystem) world to make decisions, here offshore. A certain geographic reality is hence established by the information gained from this mapping. Yet, in these specific conditions, a "missing layer" was observed: a "social and human landscape" was shaped by fishing activities. Therefore, this research on human geography aims to inform and map this “missing layer” by using exploratory, qualitative, and comparative methods. This allow to make visible another existing and observable geographic reality into the sea. This is a matter of a complex and dynamic gathering of discrete and sensible places, linked by sociotechnical fishing practices, emerging dynamic territories. Also the information and mapping of this “missing layer” therefore permits for the questioning of the real ins and outs of MSP in action, to bring to light spatial justice issues which it does emerge and to open geographic horizons for the mobilization of this other geographic reality. In sum, the information and mapping of “missing layer” are one of the multiple democratic issues of the beginning of this 21th century.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".