Assessing Representation Theory with A Framework for Pursuing Success and Failure1
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it