Affordance Networks: An Approach for Linking IT features-in-use to Their Effects
Bibliographic record
Abstract
Arguing that affordances of an IT can have a hierarchical and network structure, this paper proposes an approach to identify the actualized affordances of an IT artifact and its hierarchical and network structure for a group of users. Based on methods suggested for analyzing social networks, the paper proposes an approach to create a hierarchical network of affordances for a group of users, with affordances that are the closest to the IT artifact clustered at one end, and affordances that are closest to the IT’s consequences clustered at the other end. The resulting affordance network of the IT can provide a context-dependent image of IT usage, which helps in understanding how the IT is used and appropriated, how IT features can trigger long-term outcomes, and what consequences can be anticipated from IT for user groups. This in turn can help improve its design.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".