Extending the Social Identity of Information Systems
Bibliographic record
Abstract
Although Benbasat and Zmud’s (2003) pronouncement of an “identity crisis” within the information systems (IS) discipline has been mitigated in the industrialized world, the authors are concerned that the crisis still looms large in the developing world. The author’s objective is to theoretically underpin how the discipline can extend its social presence in developing countries to help sustain life. These arguments are contextualized with an in-depth examination of an area for which information systems research has much to offer: telemedicine. Telemedicine is an information systems intensive method concerning the remote delivery of healthcare. Telemedicine is fundamental to any healthcare solution in Sub-Saharan Africa (SSA)— a capital-starved society, home to 33 of the 48 least developed countries of the world, and suffering from a dire shortage of medical professionals. The social, political, and economic idiosyncrasies of SSA require a different lens to investigate telemedicine to induce social development. This paper proposes a research framework for telemedicine transfer in the context of SSA with propositions pertinent to the developing world. The authors draw on thorough implications of this research agenda as a stepping stone to recreate a social identity in developing nations plagued with more immediate concerns surrounding basic human sustenance.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.049 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.003 | 0.004 |
| 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".