Bibliographic record
Abstract
The advent of new technologies in organizations has created unprecedented challenges for professionals and managers alike to explore several cost-effective technology solutions aimed at improving communications among their target audiences. One such technology, which is gaining popularity, is streaming media. Streaming media has been around for approximately 10 years, but only now is becoming the fastest growing IT sector, with real revenues and a bright future (Alanko, 2004). Streaming media is a delivery technology that has great potential for enhancing the way people communicate and share information. The evolution of streaming media has made huge strides in the world of the Internet, from a medium which delivered unstable video streamed very slowly through inadequate networks, to one today that rivals the reach in some cases of small cable television channels and local market radio stations. The use of streaming media is becoming a mainstream communications tool in the public sector. It has the ability to enhance communications both internally and externally (i.e., important announcements, bulletins, community outreach, online learning, training, etc.). Many government departments have started offering live and archived Webcasting of numerous government meetings and programs via the Internet. Whereas outreach has previously been strictly limited to certain individuals, information is now being made available to a wider audience through the use of streaming media.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.016 |
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".