COLLABORATION IN GEOMATICS: MOVING FORWARD
Bibliographic record
Abstract
The subject of collaboration comes up frequently within the Canadian and international Geomatics communities. The reasons for seeking more collaborative approaches within our field are clear: we stand to become more successful in our projects, programs, research efforts and business pursuits. As Geomatics practitioners, we are also better positioned to play a leadership role in meeting the challenges of our changing world. We have learned a great deal regarding methods that foster collaboration in the use and management of geographic information. Entire programs have been created with the primary purpose of overcoming barriers to sharing geographic information. Initiatives of this nature have enjoyed varying degrees of success by implementing innovative strategies, processes and organizational elements that support and advance collaborative environments. But how far have we really come through these efforts? Despite our hard work and good intentions, the fact is that many silos still exist within and between organizations and jurisdictions. Why does this problem persist and how can we attempt to realize the full benefits of collaboration? This paper examines a selection of Geomatics studies, projects and programs in Canada and abroad where fostering collaboration is a key element. The issues, challenges, objectives, methods and outcomes in each case have been reviewed for the purpose of creating a framework for successful collaborative efforts within the Canadian Geomatics community.
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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 | 0.050 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.027 | 0.042 |
| Scholarly communication | 0.036 | 0.038 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".