Digital exchange compromises: Teetering priorities of consumers and organizations at the iron triangle
Bibliographic record
Abstract
Abstract Societal well‐being is challenged by the complexity and intangibility of the compromises inherent in digital exchanges. Increasingly these exchanges rely on technology, with competing priorities that challenge cooperation and communication among key parties involved. The authors examine the factors that drive tensions between consumers and organizations in digital exchanges, as well as how and why interest groups, lawmakers, and bureaucrats (also known as the “iron triangle”) try to mediate these exchanges through policy and regulation. By explicating the nature of these relationships, the authors illustrate various trade‐offs faced by all parties and depict a novel, comprehensive framework to facilitate holistic assessment of the factors underlying these ubiquitous but complex digital relationships with vague ethical stewardship. This framework serves as a lens to help guide business and regulatory policymaking and as a platform for identifying future research opportunities.
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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.026 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".