The Dark Side of Social Media: A Reality Becoming More Contemporary by the Day
Bibliographic record
Abstract
Social media was started with the intention of expelling the darkness from the lives of people by way of sharing knowledge, networking and communicating with near and dear. It all started with the purpose of imparting its benefits to all and making it user friendly. But the liberation of the down trodden, intended to voice their opinions, was turned into a hysterical chaos given the liberal mindedness of the people. What started off as a source of knowledge turned people into gadget freaks, attention seeking, financial and societal deficits. Given the nature of social media combined with the need to be social has turned it into a platform to mirror the human’s self-image. The world has become a ball of information enabling people to never look or stop anywhere. The world is alive and networked 24/7. The digitalization of media turned out to be a flourishing business, especially through social media, smartphones, tablets and apps. A few billion people are always online and it is expected that heaps of devices and sensors will be available on the web soon.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.028 | 0.041 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".