Small town identity and history's contribution to a response in policy change: a case study of transition from coal to biomass energy conversion
Bibliographic record
Abstract
In 2002, the provincial government of Ontario first announced plans to close all coal-burning thermoelectric generating stations. Facing the loss of local jobs should the local generating station close, Atikokan, Ontario, residents responded. This research seeks to answer the following question: What are Atikokan's historical pre-conditions and residents' attributes and perceptions which likely lead to the community's response, and do these characteristics relate back to the broader body of knowledge? Our study investigates the Atikokan Generating Station (AGS) conversion from coal to biomass wood pellets as a case, exploring the extent to which the community's identity played in response to the policy change. The current study takes a qualitative data analysis approach utilizing interviews with community members, current newspaper articles, past relevant consultant reports and archival data. Data collected were coded to themes using NVivo 10 software. Four emergent themes were identified and cross-validated. The emergent themes are i) traditions of resource-based industry congruent with producing and burning forest-based renewable fuels, ii) historical linkages to a strong entrepreneurial ethic, iii) community members' recognition of AGS's multifaceted role in the community and iv) strong community spirit and desire to fight for their town. These themes appear to have been prerequisite in order to successfully engage provincial government, and we demonstrate that these findings are somewhat corroborated back to the broader literature. Furthermore, as power generating authorities elsewhere grapple with demands to reduce carbon emissions, the Atikokan case may provide insight for other jurisdictions evaluating renewable energy adoption.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.017 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".