Evaluating the Cultural Anthropology of Artefacts of Computer Mediated Communication: A Case of Law Enforcement Agencies
Bibliographic record
Abstract
The renowned orientations of cultural models proposed by Hall and Hofstede has been the subject of criticisms. This is due to the weak, inflexible and old-fashioned nature of some designs resulting from them. In addition, is the ever-changing, formless and undefined nature of culture and globalization. Consequently, these vituperations have resulted in better clarifications when assessing the cultural anthropology of websites. Based on these later clarifications and other additions, we seek to evaluate the cultural heuristics of websites owned by agencies of the Nigerian government. Note that this is verily necessary because older models did not include Africa in their analyses. Specifically, we employed the online survey method by distributing questionnaires to different groups of experts drawn from the various regions of Nigeria. The experts employed methods such as manual inspection and use of automated tools to reach conclusions. Afterwards, the results were assembled and using the choice of a simple majority, we decided whether a design parameter is either high or low context. Findings show that websites developers tend to favor low context styles when choosing design parameters. The paper attempts to situate Africa in Hall’s continuum; therein, Nigeria (Africa) may fall within French Canadian and Scandinavian and/or within Latin and Scandinavian for the left hand and right hand side diagram respectively. In future, we would study the cultural anthropology of African websites employing the design parameters proposed by Alexander, et al.
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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 | 0.026 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.016 | 0.027 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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".