Rational Dissent on Climate Change in Calgary: responding to Haney (2022)
Bibliographic record
Abstract
Social scientists have been active participants in assessing and debunking climate change denialism. This paper applies the Scientific Method (Armstrong & Green 2022) to deconstruct the work of Haney (2022) who theorized that the massive 2013 flood in the City of Calgary, home to many oil sands and oil/gas industry head offices, would be understood by those well-to-do energy industry executives, living alongside the rivers, as evidence of climate change. Haney’s analysis of interviews with 40 such individuals assumes that it is a case of confirmation bias related to their livelihood or fear of change that these people continue to hold dissenting views on climate. This paper will expose the bias, flaws, and lack of research integrity in Haney (2022). Stirling, the author of this paper, acknowledges a direct connection to Friends of Science Society, referred to by Haney, though this is an independent work of hersstudy aimed to investigate the predictive factors of stress on parents of primary school students during the Covid-19 pandemic in Oman.A total of 384 parents of primary school children participated in this study. By using survey data technique and was analyzed by using quantitative research method. The results showed that there is a significant difference in the level of stress between employed and unemployed parents. In addition, the study found a significant difference in the stress level between parents with different educational levels. Based on the findings and analysis of the data obtained, COVID-19 has been determined to influence students’ learning negatively. It has created an unpleasant atmosphere for them in this area. It has been discovered that children and their parents are confronted with various problems and hurdles, in addition to a poor education level and parental stress as a result of a lack of resources at home.The study recommends that the government and educational institutions provide support to parents, especially those who are unemployed or have low levels of education
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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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.021 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.001 | 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".