Report from the Workshop \n“Memorial University - Community Research \nPartnerships: Resource Management in Marine \nand Freshwater Environments”
Bibliographic record
Abstract
From 21 to 22 of August 2008, the Leslie Harris Centre of Regional Policy and \nDevelopment hosted a workshop at Eastport, Newfoundland. The workshop was entitled \n“Memorial University – Community Research Partnerships: Resource Management in Marine \nand Freshwater Environments.” Its purpose was to bring together faculty and staff of Memorial \nUniversity, policy makers, and community members to discuss the issues and opportunities \nsurrounding the Indian Bay watershed and Marine Protected Areas (MPAs) (see Appendix A, \nAgenda). The workshop featured two research projects supported by the Harris Centre Applied \nResearch Fund, and consisted of presentations and question and answer/discussion periods on \nresearch partnerships in Indian Bay and MPAs, an evening presentation from Dr. Art May, and a \nsession of panel discussions led by representatives from governments, unions, and community \norganizations (see Appendix B, List of Participants). \nWorkshop participants, stakeholder organizations, and the public more generally \nspeaking will have access to this report. While the workshop itself generated considerable \ndiscussion and debate and helped to facilitate the exchange of information about both marine and \nfreshwater fisheries management, it is hoped that this is merely a beginning. That is, the aim of \nthe workshop was in part to begin a discussion, and it is hoped that participants, as well as any \nother interested parties, will provide comments and feedback both about the meeting that has \ntaken place, about future possibilities for engaging in dialogue about the management of these \nresources, and the role of university research in informing dialogue and resource management. \nGiven the diversity of the representatives at the meeting, it is not surprising that the \npresentations and panels inspired discussion of a wide array of topics and that a plethora of \nviews, interests, questions, and concerns emerged from the workshop. Generally speaking, \nhowever, a myriad of comments, questions, suggestions, and so on revolved around two basic \nthemes – governance and knowledge mobilization. The following report, therefore, is organized \naround these subjects. \n
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.188 | 0.071 |
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".