Information Privilege as Capital: An Analysis of Scholarly Communication Practices through Autoethnography and the Bourdieusian Framework
Bibliographic record
Abstract
Information privilege is the capability of people to access information that others cannot. It may affect personal concerns involving the use of information including but not limited to health, finances, and civic participation, and may also affect professional or academic pursuits which require access to information. The inequality in the way that information can be created, accessed, or distributed could also affect scholarly communication practices. As such, this research looks at the manifestations of information privilege and how they function as capital. Drawing from the author’s experiences in scholarly communication, an autoethnographic inquiry was conducted. Manifestations of information privilege were discussed and mapped against Pierre Bourdieu’s forms of capital. From the autoethnographic reflections it was inferred that Bourdieu’s different forms of capital influence scholarly communication practices in varying ways, with some forms of capital having more influence on practices compared to other forms. Some forms of capital were also seen to function potentially as both a motivating and demotivating factor in scholarly communication practice. This research reflects the impact of information privilege in the conduct of scholarly communication pursuits. It also illuminates the necessity of strengthening open access awareness along with reevaluating scholarly communication practices and related policies, to support the need to democratize knowledge and ensure that knowledge is made easily available and accessible to anyone who may need it.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.033 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".