The Impact of Social Media on Improving the Services of the Department of Lands and Survey in Jordan
Bibliographic record
Abstract
The present study aimed to identify the impact of social media on improving the services of the Department of Lands and Survey in Jordan. To meet the study’s goals, a forty-item questionnaire was developed. This questionnaire covers four areas. The questionnaire forms were distributed to 737 service recipients of the Department of Lands and Survey in Jordan. It was found that social media has a significant impact on improving the services of the Department of Lands and Survey in Jordan. It was found that Facebook, WhatsApp, YouTube, and Twitter have the greatest impact respectively on improving the services of the Department of Lands and Survey in Jordan. It was found that there isn’t any statistically significant difference –at the statistical significance level of (a=0.05)- between the respondents’ attitudes in this regard which can be attributed to (gender or academic qualification). It was found that there is a statistically significant difference –at the statistical significance level of (a=0.05)- between the respondents’ attitudes in this regard which can be attributed to (nationality). The latter difference is for the favor of Jordanians. Based on the afore mentioned results, the researcher recommends: Distributing pamphlets to the service recipients of the Department of Lands and Survey in Jordan. Such pamphlets must encourage them to follow the official page of the latter department on Twitter Responding faster to the inquiries of the service recipients of the Department of Lands and Survey that are sent through WhatsApp.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".