Evaluation of Experiences in the Canadian Conservation Corps: A Qualitative Content Analysis
Bibliographic record
Abstract
A qualitative content analysis study was conducted in partnership with the Canadian Wildlife Federation (CWF) and Mitacs to explore the experiences of participants involved in the Canadian Conservation Corps (CCC) program. 27 semi-structured interviews conducted with 14 interviewees were audio-recorded and transcribed. Interview data were analyzed using an inductive qualitative content analysis of latent data. The analysis identified seven themes which are: Theme 1: Participants Discussed the Importance of Community Theme 2: Participants Learned Through Difficulty Theme 3: Participants Experienced Personal Growth Theme 4: Participants Developed a Range of Skills Theme 5: The CCC Supported Participants’ Career Development Theme 6: Stage 2 Leaders Described Benefits to Their Organizations Theme 7: Interviewees Offered Suggestions for Improvements to the CCC Findings were shared with CWF and stakeholders of the CCC to inform funders and to support future development of the program.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".