Broken Telephone: The long and Winding Road from Encoding to Decoding in Mixed Methods Research
Bibliographic record
Abstract
It is not uncommon for engineering education researchers who value quantitative analyses to conduct large-scale surveys as validation measures for smaller scale qualitative studies. The purpose of this paper is to examine a limitation of this logic. We adopt Stuart Hall’s encoding/decoding model of communication as a conceptual framework to investigate the mismatch between five engineering career paths we identified through a small, qualitative study and 982 Canadian engineering graduates’ self-identification with those paths on a larger scale national survey. By examining the differences between our encoding of survey items and respondents' self-identified decoding of those items, our paper makes two significant contributions to the engineering education literature, one methodological and one structural. First, our findings raise methodological questions about the widespread use of large-scale surveys as validation measures for small-scale qualitative studies, and second, our critical analysis illustrates internal heterogeneity within senior executive career tracks, enabling us to supplement existing explanations for occupational inequity in the profession.
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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 | 0.620 | 0.797 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.011 | 0.073 |
| Scholarly communication | 0.028 | 0.051 |
| Open science | 0.008 | 0.022 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".