Sharing experiential information in online discussion: the case of coping with the COVID-19 epidemic
Bibliographic record
Abstract
Purpose To specify the nature of experiential information by examining how such information is articulated and shared in online discussion. Design/methodology/approach Experiential information is approached by identifying two constitutive aspects: (1) sensory information that an individual obtains from noticeable events lived through by means of human senses such as sight and touch and (2) cognitive–affective information that is indicative of how the individual interprets such events by means of cognitive operations like comparison and evaluation, as well as appraises the affective valence of such events. To examine the nature of experiential information, an empirical study was made by analysing how people articulate sensory and cognitive–affective information in online discussion about the COVID-19 epidemic. To this end, a sample of 1773 messages posted to the online forum hosted by the Canadian Broadcasting Company was scrutinized by means of descriptive statistics and qualitative content analysis. Findings Experiential information was mainly articulated in the depiction of visual observations of lived-through events, as well as in their comparison and evaluation. Experiential information was often articulated in conjunction with information of other types, most notably topic-related opinions, neutral descriptions of COVID-19 related issues and suggestions offered to fellow participants. Research limitations/implications As the study concentrated on the sharing of experiential information about the COVID-19 epidemic in an online discussion forum, the findings cannot be extended to concern the exchange of experiential information in other contexts. Originality/value The study is among the first to characterize empirically the nature of experiential information by examining the articulations of online discussants.
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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 | 0.025 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.025 | 0.020 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".