From psychological to digital disengagement: exploring the link between ageism and the ‘grey digital divide’
Bibliographic record
Abstract
The need for digital literacy is apparent in today’s workplace, driven by strong pressures for constant technological innovation. Previous studies have shown that although older workers make up (and will make up) a great proportion of the workforce, there persists an age-based digital divide in the workplace; and the outcome of such divide is quite negative: at the individual level, older workers feel they’re being marginalized and as such, become dissatisfied and disengage from their workplace; at the organizational level, a pool of skills and expertise is lost as a result of the older worker’s disengagement, putting at risk effective knowledge transfer and mentoring process. Hence, the importance of a deeper understanding of the contextual factors that may feed the ‘grey digital divide’ in the workplace. The goal of this paper is to address such factors moving beyond the ageist claim that a worker’s chronological age is the driving force behind the ‘grey digital divide’.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".