Developments in Practice XXXI: Social Computing: How Should It Be Managed?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social computing, enabled by the Internet and peer-to-peer computing (P2P), is a force to be reckoned with. Today, most observers believe that the changes we’ve seen in some industries, like entertainment, is just the tip of a huge iceberg that is going to hit many different sectors. The power of social computing to disrupt the traditional business-to-customer relationship is merely one of several changes we are beginning to see in organizations. Social computing also facilitates new ways of working, learning and collaboration, which are foreign to more conventional practices but which have considerable strategic potential if they are effectively managed. Yet currently, organizations in general do not appreciate its value and strategic potential. Social computing’s promise is that technology will fit more naturally into our lives because it will adapt more readily to our locations, preferences and schedules. The challenge for organizations is to understand how to use it effectively to deliver new forms of business value. It’s easy to dismiss social computing as “just another technology fad” and most companies are approaching it very cautiously. The reality is that social computing is already a factor in organizations today even though we are still early in its evolution.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it