The Innovative Semi-Analytical Screen Survey Tool and Intermittent Screen Review Sampling Method Used Amid COVID-19 Pandemic
Bibliographic record
Abstract
Hence the social life is changing and social interaction is amplified by technology. Therefore, social research would change its approach/es concomitantly. We are living in the age of technology where many people are interacting through social media generally referred to as a screen. Therefore, it is creating the need for innovative screen research methods to study and give meaning to screen interaction. Due to lock-down and restrictions on physical interaction during the COVID-19 pandemic, the screen interaction is intensified. Particularly the community-based organizations, businesses, and academia were observed prone towards using screen interaction approaches. Similarly an International Non-Governmental Organization hereinafter (INGO) in Erbil, Iraq. Started a Facebook page to interact with its beneficiaries to listen to their urgent needs and feedback to project activities. Based on that monitoring and evaluation unit observed a need to monitor screen interaction between organization and community. Hence, the innovative approaches of screen survey and screen sampling were identified. To conduct an intermittent screen survey it was important to select a relevant sampling method. In general, there are two schools of sampling in social sciences. Probability sampling and non-probability sampling. Under probability sampling, each individual has the right to be selected as a participant in a study. Under non-probability sampling, participants are selected based on certain criteria that are relevant to the domain of study. Both schools of sampling have many types and sub-types selected as per the specifications of a study. Therefore, the Intermittent Screen Review Sampling (ISRS) method was developed based on precedent theoretical work. The screen survey refers to the collection and analysis of responses of viewers of any specific social media page. Where respondents are not asked to participate or share their feelings or thoughts. Respondents voluntarily appear on the screen and interact with any post and reflect their thoughts. Henceforth, the surveyors collect these displayed thoughts intermittently, do some analytical work, and produce meaning out of these emojis, shares, memes, and comments. Quantitative and qualitative analyses were conducted within the context of the post/s shared by authorized person/s on a social media official page. Thereafter, the results were presented in quantities and narrations. This research paper is developed to communicate these innovative approaches of semi-analytical screen survey and intermittent screen review sampling at a wider level. This research would pave a way for further screen studies and innovations that are the needs of our screen generation.
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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 | 0.050 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".