Affordance-Based Information Technology Sensemaking [ABITS]
Bibliographic record
Abstract
Abstract Information Technology (IT) use gives rise to a wide variety of outcomes. This stems in part from the divergent ways in which individuals understand technology. While the sensemaking literature unveils how meaning is attached to organizational phenomena via cognitive and social processes, it overlooks the discovery dimension of making sense, that is detecting the role of the IT artifact in bringing about outcomes. In other words, there is a need to explain how the IT artifact contributes to technology sensemaking and its outcomes. This paper presents a framework that enables scholars to analyze the IT artifact’s role in technology sensemaking and its outcomes. The paper proposes an Affordance-Based IT Sensemaking (ABITS) framework that explicates IT sense-made as a distinctive ontological arrangement among the users’ perceptions of technology affordances, the affordances that users actualize, and the user characteristics that underpin optimal adaptation. The study shows how these sense-made configurations lead to outcomes for individuals and organizations. This conceptual combination allows for the examination of user appropriations of new technology, as well as the integration of the IT artifact into accounts of IT sensemaking and its outcomes.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".