The Social Media Communication of Hungarian County Seats: Facebook, Instagram, and YouTube Presence
Bibliographic record
Abstract
Abstract The communication toolkit of urban marketing has changed significantly in recent years, with online solutions and social media becoming the focus of attention besides (and, in a way, instead of) classic offline tools. In our study, we explore how this toolkit can be effectively applied to cities and how cities should communicate through different platforms. For this purpose, we have created a kind of social media tutorial regarding Facebook, Instagram, and YouTube. In our own primary research, we used data from the first quarter of 2021 to investigate the presence of Hungarian county seats on the abovementioned three platforms. For this purpose, in addition to the usual social media data, such as page likes, subscribers, number of views, or even the activity rate, we created a much more complex, professional but also – inevitably – somewhat subjective analysis system. It would be also worthwhile for other cities to use this criteria system as a checklist or to adopt good practices from the cities at the top of the list.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".