From Use to Effective Use: A Representation Theory Perspective
Bibliographic record
Abstract
Information systems must be used effectively to obtain maximum benefits from them. However, despite a great deal of research on when and why systems are used, very little research has examined what effective system use involves and what drives it. To move from use to effective use requires understanding an information system's nature and purpose, which in turn requires a theory of information systems. We draw on representation theory, which states that an information system is made up of several structures that serve to represent some part of the world that a user and other stakeholders must understand. From this theory, we derive a high-level framework of how effective use and performance evolve, as well as specific models of the nature and drivers of effective use. The models are designed to explain the effective use of any information system and offer unique insights that would not be offered by traditional views, which tend to consider information systems to be just another tool. We explain how our theory extends existing research, provides a rich platform for research on effective use, and how it contributes back to the theory of information systems from which it was derived.
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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.031 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".