Bibliographic record
Abstract
Information is a valuable commodity, but only if it is shared. Through diversified technologies, the dissemination of information has been made possible for a number of government organizations around the world, but for some, developing efficient and effective e-government systems poses a variety of unique challenges. Key demographic and economic variables, such as income, education, language, human resources and lack of appropriate products and robust regulatory frameworks for information and communication technologies (ICTs) drive the policy questions surrounding electronic commerce in government operations. These variables are important because they are the most likely to have a differential impact on the consequences of delivering new and progressive ICTs to various segments in developing countries. Described and discussed are the advantages and limitations of streaming media technology, a form of new ICT, and the comparative benefits it has in both developing and developed countries. Indian and Northern Affairs Canada (INAC) serves as a point of reference, as for the role and impact ICT-specifically streaming media–can play-within a government sector. With limited resources,INAC, a Canadian federal government department, has improved access to information and enhanced communication by successfully executing streaming media technology in-house. The implementation of streaming media technology at INAC has resulted in a fundamental transformation in the nature of information and communication exchange within the organization.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".