Bibliographic record
Abstract
Purpose While governments have invested in broadband infrastructure to ensure universal access, researchers argue that infrastructure alone does not guarantee internet use. The purpose of this paper is to investigate the effectiveness of one such government initiative on households’ internet adoption and use. Design/methodology/approach The authors used data from 2002 to 2014, including two choice experiment surveys and broadband access and subscription data. Findings The results of Survey 1 show that urban households valued existing e-services more than rural households, indicating the importance of government investment in broadband access. The results of Survey 2 show that when a publicly funded new broadband network equalized access costs, rural households valued overall e-services more than urban households, highlighting the dual role of access to e-services and their perceived benefits. Importantly, these results suggest that rural households resist social change, which lowers their valuation of certain new publicly funded e-services. Research limitations/implications These findings extend the digital divide literature by providing empirical support for the applicability of the global village vs urban leadership framework in households’ valuations of e-services. Practical implications While the government has worked diligently to enhance access, it also needs to focus on the types of content and services and better communication with communities. Originality/value Recent research has focused on inequities in skills and usage, not internet access. Furthermore, the authors examined the inequality in benefits of access to meaningful e-services and better communication with beneficiaries.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".