Automated content analysis as a tool for research and practice: a case illustration from the Prairie Creek and Nico environmental assessments in the Northwest Territories, Canada
Bibliographic record
Abstract
Public engagement is essential to the procedural and substantive sustainability of environmental assessment. Public hearings present the lowest barrier to entry for public participation, but these forums face competing political pressures for conducting appropriate public engagement within an expeditious process. Repositories of public hearing testimony provide a source of primary data for examining these public engagement issues during environmental assessments. However, the time and resources required may be prohibitive for conducting the kind of in-depth qualitative analyses that are commonly used. Automated content analysis (ACA) techniques can provide a rapid, replicable, inductive, and systematic way to examine public hearing transcripts, consisting of the critical development and application of computer programming scripts that synthesize evidence from extensive document sets. This case illustration demonstrates the potential utility of ACA, based on the examination of two public hearings, Prairie Creek (EA0809-002; 2008–2011) and Nico (EA0809-004; 2009–2013) conducted in the Mackenzie Valley, Northwest Territories, Canada. Our interpretation of the findings provides an evaluation of ACA methods and situates its potential to inform environmental assessment research and practice across jurisdictions.
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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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.031 | 0.014 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".