Falling Through the Cultural Gaps? Intercultural communication challenges in cyberspace.
Bibliographic record
Abstract
In this paper we report findings of a study of online participation by culturally diverse participants in a distance adult education course offered in Canada, and examine two of the study’s early findings. First, we explore both the historical and cultural origins of “cyberculture values” as manifested in our findings, using the notions of explicit and implicit enforcement of those values. Second, we examine the notion of “cultural gaps” between participants in the course and the potential consequences for online communication successes and difficulties. We also discuss theoretical perspectives from Sociolinguistics, Applied Linguistics, Genre and Literacy Theory and Aboriginal Education that may shed further light on “cultural gaps” in online communications. Finally, we identify the need for additional research, primarily in the form of larger scale comparisons across cultural groups of patterns of participation and interaction, but also in the form of case studies that can be submitted to microanalyses of the form as well as the content of communicator’s participation and interaction online.
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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.023 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| 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".