Digital selves, digital scholars: Theorising academic identity in online spaces
Bibliographic record
Abstract
As the digital world becomes enmeshed with our physical world, identities become public by default, and this can have disastrous consequences for those whose digital identities are deemed socially unacceptable. For scholars, considerations of public identity are especially critical, as academia functions in many ways as a reputational economy (Willinsky, 2010). Thus, while concerns over digital footprint are widespread amongst the general population, they become particularly pressing for academics, but avoiding digital spaces entirely is increasingly a non-viable option as institutions of higher education expand into digital domains. As well, there are many affordances made possible by various forms of digital scholarship (Veletsianos and Kimmons, 2012). Many scholars are therefore tasked with the necessity of navigating a digital culture that is quick to judge and reluctant to forgive. In this paper, we theorise the ontological foundations of (digital) identity in order to better understand the complexity of academics’ online participation. We explore the conceptualization of identity as fixed and unitary or as a coherent whole from which we might select ‘acceptable identity fragments’ to present in public online spaces (Kimmons and Veletsianos, 2014). Then, employing a poststructural lens, we theorise the effects of such a modernist epistemology on digital identity and scholarship, including the repercussions of seeing identity as fixed, unitary, and controllable on diverse digital phenomena: cultural hysteria around the permanence of digital footprints; a decreased collective capacity for forgiveness as we lose the ability to forget past misdeeds (Ambrose, Friess, and Van Matre, 2012); increasing occurrences of cybervigilantism in response to acts taken out of context (Ronson, 2015). Finally, we theorise the possibilities and challenges offered by a reimagining of digital selfhood in poststructural terms, as fluid, never complete, and conferring a constrained agency.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".