Good practices guide: Success in building and keeping an aboriginal mapping program
Bibliographic record
Abstract
The "Good Practices Guide - success in building and keeping an Aboriginal mapping program" profiles practices that lead to success when implementing geomatics programs in Canada. The project team undertook a literature review including professional and scholarly research on factors for success when putting geomatics programs into operation, both in Canada and internationally. Findings were combined with the pooled knowledge of the report authors, who have over sixty years combined experience in this sector in many communities and organizations. A list of potential success factors was developed and a survey questionnaire based upon these was written. Representatives of Aboriginal organizations across Canada that were running or had operated local mapping programs were invited to fill out the questionnaire and participate in follow up interviews so they could share lessons they had learned while setting up and managing their programs. Their input was used to refine the list and complete a final list of good practices. Practices and advice are grouped under six headings: getting started; gaining leadership and community support; funding and finances; human resources and training approaches; technology, data, and data networks; and support networks. Under these, specific concrete points of advice on principles for success are provided. Additionally, examples from first hand experiences are shared in a case study format to highlight specific principles in action. The guide is not intended to provide a single-track road to success. Aboriginal mapping programs are as diverse as the communities themselves, and some principles presented will apply to some communities more than others. Taken as a whole, the guide should be useful to leaders responsible for setting up and managing programs, and for information technicians with responsibilities for mapping.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".