The Effectiveness of environmental communication with scuba divers : a case study comparing the curricula of BSAC, PADI, and SSI entry-level certification courses
Bibliographic record
Abstract
The purpose of this study was to determine the effectiveness of the \nenvironmental communications contained in novice certification course manuals. This \nwas accomplished by reviewing the effectiveness of these communications across three \ncertifying bodies, the British Sub Aqua Club (BSAC), the Professional Association of \nDiving Professionals (PADI) and Scuba Schools International (SS I), using the \nframework of the Elaboration Likelihood Model of Persuasive Communication (Petty, \nMcMichael & Brannon, 1 992), and its associated message delivery styles, as a \nframework for evaluation.The messages conveyed to scuba divers through each agency' s novice \ncertification course manual were analyzed using software-assisted content analysis. The \ncontent analysis examined the manifest and latent content to determine the overt and \ncovert messages inherent within the texts. This was accomplished using a mixture of \ninductive emergent category development and deductive category application. Images \nwere also coded to indicate whether they supported or contradicted the environmental \nmessages espoused in the manual. All written messages were assigned codes that \nindicated the message delivery style, and route to persuasion, used. Once each certifying \nbodies' textbook had been systematically coded, and categories I themes had been identified, a comparison of the environmental messages communicated across certifying \nbodies was undertaken to determine the manuals' relative efficacies.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".