The Psychology of the Internet at Work
Bibliographic record
Abstract
The Internet has had a dramatic impact on almost every aspect of our lives, changing the way that we communicate with others, purchase products, receive healthcare, and conduct training programs. It has also had a profound influence on organizations. Further, the Internet has changed the nature of work (e.g., use of virtual teams), and the interaction between supervisors and subordinates. Hertel, Stone, Johnson, and Passmore (2017) suggest that Internet- based work is characterized by five key characteristics: enhanced accessibility of information, interactivity of communication, automatic storage of work-related data, automatization of work processes, and boundary crossing work. That is, standardized communication tools do not simply connect different work processes at different geographic or organizational locations, but it can also integrate non-work domains, and machines into remote communication networks. Despite the widespread use of the Internet in work contexts, relatively little theory and research has examined how these characteristics have impacted work. Practitioners are implementing these systems without the benefit of research. The goal of this symposium therefore is to bring together scholars from OB, HR, leadership, and information systems to review the existing theory and research on e-recruiting, e-learning, virtual teams, e- leadership and the use of social networking sites in the employment process and discuss their implications for work practices and software design. E-recruiting: The Impact of Technology on Recruiting Research and Practice Presenter: Derek Chapman; U. of Calgary Toward an Integrated Model of the Factors Contributing to e-Learning Effectiveness Presenter: Richard Johnson; U. at Albany, State U. of New York Presenter: Kenneth G Brown; U. of Iowa As Virtual Teams Become Even More Popular, What do We Really Know about What Makes Them Work? Presenter: M. Travis Maynard; Colorado State U. Presenter: Lucy L. Gilson; U. of Connecticut E-Leadership: What is Different, Literature Review, and Future Directions Presenter: Surinder Kahai; Binghamton U.-State U. of New York Search Engines, Social Networking Systems, and Employment Decisions Presenter: Kimberly Lukaszewski; Wright State U. Presenter: Andrew Franklin Johnson; Texas A&M U., Corpus Christi
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".