Investigating complex-value-based community mine education strategies : a case study with the Tlicho community in the Wek’eezhii region Northwest Territories, Canada
Bibliographic record
Abstract
This thesis attempts to define an effective model for developing educational programs related to mining for communities in an iterative manner that speaks to the needs and values of the community. The lessons learned provide direction on methods to replicate an effective education program for communities. The success of a process that was used to develop an education program in a mining context for a particular case has been evaluated. Reasons for why learning does or does not transpire for this case were explored. A case study of the Tlicho community in Canada’s Northwest Territories was conducted to investigate this query. A combination of theories, approaches, and methods were utilized in the development of the education program, the collection and interpretation of data, and the formation of key findings. The inquiry led to the following four key conclusions: 1) Knowledge and understanding are effectively acquired by situating information as primary experiences or through oral accounts by persons who have experienced. 2) The objects of learning for education programs must be valuable, useful, and meaningful to the intended learners. Each individual must be given the autonomy to decide what topics or concepts are appropriate for him or her. Thus, choice and flexibility must be built into the programs. The “I am going to teach you...” approach to education is less superior than a humble humanistic approach to education. 3) The process to develop programs should involve cycles of action and reflection, input from the intended learners, and repetition. 4) Assimilation of information occurs through the experience of knowledge that is presented in culturally based frames informed by particular stories, experiences, teachers, places, values, histories, and materials. These conclusions provide some insight on how governments and mining companies can and should engage with communities to learn. Enhanced knowledge and understanding through learning by communities, governments, and mining companies, strengthen relationships and agreements. When everybody’s knowledge and understanding improves, better decisions can be made.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".