Assessing Representation Theory with A Framework for Pursuing Success and Failure1
Bibliographic record
Abstract
Representation theory (RT) is one of few long-standing, native theories in the Information Systems discipline. Over the past 30 years, RT has spawned a wide program of research, primarily on modeling of information systems but also on other phenomena such as data quality, system alignment, security, and effective system use. Nonetheless, descriptions of RT are splintered across many papers over many years. RT has also attracted repeated criticisms about assumptions, tests, and results. As a result, the nature of RT, its merits (or lack thereof), and how best to progress it, are unclear. Motivated by these issues, this paper provides a much-needed overview of RT. It further offers an evaluation of RT and explains how research on RT can improve, using a novel framework for evaluating theoretical programs. Our analysis shows that RT’s merits (or lack thereof) remain inconclusive because prior research has not proceeded systematically enough. In this light, we explain and illustrate how research can proceed more systematically.
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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 | 0.071 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".