The importance of <scp>socio‐emotional</scp> considerations in online communities, social informatics, and information science
Bibliographic record
Abstract
Abstract Alongside cognitive and social phenomena, many scholars have examined emotional and affective considerations in information science, but a potential emotional or affective paradigm has not coalesced to the extent of the social or cognitive paradigms. We argue information science research should integrate the social paradigm, as offered by social informatics, with affective and emotional considerations: a socio‐emotional paradigm. A review of existing literature and findings from users' motivations to participate on the Academia section of the Stack Exchange social questioning‐and‐answering site make our case. We uncovered tensions between the intended information‐centric focus of the community and users who believed social, emotional, and affective considerations needed to be foregrounded, speaking to online communities acting as boundary objects, with the “fit” for one user or community not always the same as for another. An integrated socio‐emotional paradigm shows much strength for social informatics and information science research, including uncovering hidden concerns and differences in values, as in our study. Affective and emotional research, often bubbling under in information science, should rise to the surface is not so much a paradigm shift but an integration of social, emotional, and affective considerations into a socio‐emotional paradigm.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.046 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".