GLOBAL VILLAGE OR URBAN JUNGLE: CULTURE, SELF-CONSTRUAL, AND THE INTERNET
Bibliographic record
Abstract
Cultural psychologists have known for some time that self-perception or self-construal is mediated in large measure by cultural boundaries and structures like geography: for example, that agrarian and collectivist cultures are more interdependent than so-called Western or individualistic cultures. Mod-ern communication technologies like the Internet are blurring the distinctions between cultures and tearing down geographic boundaries, creating questions about the implications for the psychology of self. In this paper are addressed the psychological implications of the global network as a cultural context and whether the Internet promotes an individualistic or interdependent sense of self. T IS a safe assertion that modern communication technologies like the Internet have the poten-tial to tear down geographic boundaries and blur the distinctions between what have been thought of as traditional cultures. So what are the implications for those cultures and the indi-viduals therein? What are the implications for the psychology of self? In this paper I would like to address the psychological implications of the global network as a cultural context and whether the Internet promotes an individualistic or interdependent sense of self. This paper is an attempt to sort out a number of ideas in preparation for an empirical study of the relationship between self-construal, culture, and Internet use. I will begin with a cursory examination of current Inter-
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".