The virtualization of free space and action : advancing a mondernized model of power inequity attenuation and the Free Space Index (FSIx)
Bibliographic record
Abstract
Although international electronic technologies have not provided a direct portal to a Utopian world of fairness and equality as some had dreamed, they have important implications for socio-organizational power that have thus far been under-considered. Once a power structure is in place, it is generally self-reinforcing — powerful actors have motivation and wherewithal to subordinate others, and less powerful actors are constrained from resisting. However, when a technology is introduced to a social system, it creates opportunities for interaction patterns governing power within that system to evolve. International electronic technologies create new and rapidly changing, virtualized contexts for computer mediated communication, social media broadcasting, social networking, coordination, and action. These contexts erode geographic, social, and psychological boundaries that have traditionally determined how, and if, power would be utilized, accepted, resisted, or challenged. In this dissertation I present a modernized model of power that takes these changes into account and report six related empirical studies. In advancing my model, I also draw from, refine, and extend free space theory. I argue that these technologies embed sheltered interaction contexts where the less powerful can express themselves and interact more freely. These spaces can spawn social movements and other forms of collective resistance and ultimately result in social- and/or organizational change. In Studies 1-3, I create the Free Space Index to identify such spaces both online and offline. I collect data in Canada, the USA, and Denmark for cross-societal validation. Studies 4-6 test two central propositions underling my model. The first is that electronic technologies discourage some power-reinforcing behaviours by raising perceived retribution risks; Study 4 examines this in an organizational decision-making context. The second is that these technologies promote action challenging prevailing power structures. Study 5 shows the effect of self-interest, a key offline action predictor, differs online. Study 6 demonstrates that electronic technologies promote action by reducing participation costs—congruent with the slacktivism moniker often applied to Internet mediated social activism—but also by attenuating a number of socio-psychological constraints that discourage offline action. I discuss the implications and limitations of my model and empirical work and suggest future research directions.
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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 | 0.000 | 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 | 0.000 |
| 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 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".