The Impact of Digital Communication Technologies and New Remote-Working Cultures on the Socialization and Work-Readiness of Individuals in WIL Programs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Social media, network capabilities, and digital communication technologies are changing the nature of work for individuals in WIL programs; further challenging the connections between industries and universities in their efforts to ensure individuals are work ready. However, digital technologies have provided new resources to help individuals socialize into the workplace and develop new skills for meeting the challenges of the information age that will also impact on how they get a job, and then do that job. The current literature on WIL, organizational behavior, and remote working, provides a theoretical framework for identifying the key points on the transitions experienced by individuals through WIL using the prism of social media, digital technologies, and the changes in work culture through remote working. Key issues in relation to transition are illustrated using two examples: one French and the other Canadian. The French study examines the effects of social media and digital technologies on individuals in WIL programs in relation to developing work readiness skills and communicating with supervisors and coworkers. The Canadian example examines the challenges internship students face when their workplace is predicated on remote working. The impact of social media, digital and communication technologies present new challenges for fulfilling the objectives of WIL programs and ensuring students are ready for work now and in the future.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it