Bibliographic record
Abstract
New emerging technologies and the continuous process of digitisation around the globe means a new model of civilization is emerging where society is "digitized" or "datafied".This paper analyses to what extent we have undergone the process of digitisation and datafication through understanding concepts of the surveillance society, spectacle society, our digital footprints and the amount of data gathered by government agencies, individuals and companies and its utilisation; and how this process has led to the development of a "global digital civilization".Disparity between levels of digitisation in different regions in a globalised world are addressed to determine if certain regions contend hegemony over others within digital realms.To further understand how globalisation itself is perceived in a digitised world, a software called "Mention" was used to collate data in relation to the terms "globalization", "global" and "globalisation" on digital platforms such as Twitter, blogs, news, websites and forums over a month's time period.Online rhetoric in relation to this term and the locations from which data was mostly surfacing in higher amounts has greatly added to the findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.057 | 0.005 |
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".