An Argument-Based Analysis of Communicating Academic Findings to Practitioners
Bibliographic record
Abstract
The AIS InPractice merged with Science2Practice in 2020. The new institution now translates articles published in AIS journals into Insights to publicize academic findings. Similarly, MISQ and ISR have a joint initiative with Sloan Management Review to translate academic papers into articles published in SMR. Despite these significant efforts in the IS field, little has been done to investigate whether academic findings have been communicated effectively to practitioners by these disciplinary endeavors. Against this backdrop, we apply Toulmin’s argument framework to discuss two different ways to support a knowledge claim, i.e., warrant-establishing and warrant-using. We then conduct an empirical study to examine how leading academic journals in the IS field rewrite their published articles for a practitioner audience. We found two problems in these communications: First, when academic articles are rewritten as warrant-establishing arguments, scientific methods are often not included. Second, when academic articles are rewritten as warrant-using arguments, the findings are not compared to existing practices. Based on our analysis, we propose that future communication could follow a knowledge flyer style focusing on comparing academic findings with existing practices. Also, we propose a Master of Philosophy program which could be an effective pedagogical effort to educate IS practitioners.
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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 | 0.054 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".