Conceptualizing the (non) users of the internet
Bibliographic record
Abstract
Most studies about internet use examine how usage differs among users and why. Less attention has been paid to the varied degrees of non-use or low levels of use. Non-adopters of digital media are usually understood as not having access to digital media. However, there is a considerable variation among them with regards to how and why they lack the connectivity. Furthermore, it is important to acknowledge those who do have access but use the internet only in a limited capacity. Digital exclusion does not only occur among those who do not have access but expands to those who cannot use the internet effectively. A new type of digital exclusion is emerging due to this variation of usage and appropriation. We propose a nuanced approach in defining the various levels of internet non- and low use. Rather than highlighting how social exclusion, therefore the lack of connectivity, leads to digital exclusion, this paper looks at the various contexts in which people might be digital disengaged and therefore digitally excluded.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.008 |
| 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".