Bibliographic record
Abstract
In this paper, the reflexive relationship between social interaction and understanding of self in online learning networks is examined. In keeping with constructionism, we acknowledge the significance of social interaction in learning and identity formation. It is through identification with and differentiation from others that individuals are able to establish their sense of self. Therefore, a sense of self is inherently connected to one's sense of belonging within a community (-ies). Building on the work of Goffman (impressions management) and Foucault (technologies of the self), a model of identity and community formation is introduced: the Web of Identity (WoI). According to the WoI model, community members rely on technical, structural, political, cultural, and idiosyncratic perspectives coupled with performance strategies in a continuous cycle of internalization, understanding, enactment, and revision of their individual identities. We discuss how individuals construct their identities through relational dialogue and interaction in which they express, share, and build upon their histories, practices, and goals. It is an ongoing reciprocal process that constantly changes both personal and collective narratives. The combined perspectives and strategies guide the individual's behaviour, but do not completely restrict it. The individual is free to choose how to perform and may choose performances that support or contravene accepted social practices. Ideally, this process guides the individual towards cognitive resonance, a process in which he/she behaves in accordance with his/her internalized conceptions of society and self. If an individual's actions do not resonate with social expectations, accepted practices, and self, the individual may modulate his/her actions or may cause shifts in accepted practices, expectations, or self identity. Such shifts filter through the WoI performance strategies. The paper concludes with suggestions for further research in identity formation in online learning networks. We recommend study of current social networks and learning management systems to identify existing mechanisms that permit expression of WoI strategies. In addition, we recommend research into how social software systems can be developed or facilitated to encourage strategy use. Since, it is through these performances that the individual enacts his/her identity, we wish to know how, in actual practice, strategy use will affect conceptions of self and community in educational environments.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".