Who says what to whom? Assessing the alignment of content and audience between scholarly and professional publications in technical communication (1996–2013)
Bibliographic record
Abstract
Academe-industry relations are an ongoing topic in the conversation on technical communication. Key issues in the conversation include alignment between academic curricula and industry needs, the effectiveness of the preparation provided by academic programs, and the alignment of interests between the two groups. However, no study has attempted to empirically assess the extent of the academic and industry alignment empirically. We explore this issue here and are guided by the following questions: (1) What content areas are covered by both peer-reviewed and trade publications?, (2) What content areas are unique to each type of publication?, and (3) Who is the intended audience of the content? To assess this alignment, we coded for three major content areas in a random sample of 348 articles published between 1996 and 2013 in four leading peer-reviewed Journals (IEEE Transactions on Professional Communication, Journal of Business and Technical Communication, Technical Communication, and Technical Communication Quarterly) and one publication for practicing technical communicators (Intercom). Results suggest that professional publications tend to publish process-oriented articles, articles focused on technology and professionalization, and articles intended for writers/content developers. Scholarly publications tend to publish product- and education-oriented articles, articles focused on assessment and research design, and articles intended for academics. This, in turn, provides insights into differences in the conversations of practicing professionals and academics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.051 | 0.204 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".