Bibliographic record
Abstract
年 7 月に行われた European Survey Research Association(ESRA)のカンファレンスでは，インタ ーネットを利用した調査における工夫や課題について多くの報告が行われた．Dillman が提唱する Push-to-Web を用いることにより， インターネットを利用した Mixed-Mode 調査でも回収率を高めることが できる．カナダの国勢調査ではネットによる回収が約 7 割を占めた．オンライン調査におけるモバイル端 末への対応に関する工夫も報告された．画面サイズが小さいモバイル端末に不向きなグリッド形式の質問 の欠点を解消するため，de Leeuw は Horizontal Scrolling Matrix(HSM)形式を，Barlas らはアコーディ オングリッド形式を提案している． また， オンライン調査では， スマートフォンによる回答が増えており， 質問文や画面レイアウトなどは「モバイルファースト」で作成するべきだという意見もあった．本稿は， 日本におけるインターネットを活用した調査方法論の発展に資するため，これらの知見を整理して論じる ものである． At the European Survey Research Association (ESRA) conference held in July 2017, there were many reports on contrivances and issues with regards to Internet surveys.By using the Push-to-Web for which Dillman advocated, it is possible to increase response rates using the Internet, even with Mixed-Mode surveys.In the Canadian census, Internet responses accounted for approximately 70% of responses.Contrivances that used mobile devices in online surveys were also reported.De Leeuw proposed the Horizontal Scrolling Matrix (HSM) format and Barlas et al. proposed the accordion grid format to solve the weak points in grid format questions unsuitable for mobile devices with small screen sizes.Furthermore, smartphone responses increased in online surveys.Therefore, there were also suggestions that questionnaires and screen layouts should be created with "Mobile First."We organize these findings to discuss and contribute to the development of Internet-based survey methodologies in Japan.１. はじめに ２．Mixed-Mode 調査 ３．Push2Web ４．確率パネル ５．オンライン調査でのモバイルデバイスへの対応 ６．非確率オンライン調査のデータ品質 ７．混合デバイス調査 ８．モバイルデバイスでの新しい測定機能 ９．パッシブモバイルデータの収集 10．ビッグデータと調査 11．おわりに 29 政策と調査 第14号(2018年3月)
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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 | 0.094 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.037 | 0.025 |
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".