Bibliographic record
Abstract
Purpose The purpose of this paper is to introduce the notion of social search as a new concept, drawing upon the patterns of web search behaviour. It aims to: define social search; present a taxonomy of social search; and propose a user‐centred social search method. Design/methodology/approach A mixed method approach was adopted to investigate and conceptualise the notion of social search. A review of literature on social search was carried out to identify the key trends and topics. A model of online collaboration was adopted to delineate the types and categories of social search. Four use case scenarios were developed to provide a more pragmatic approach to the understanding of social search. Findings The developed taxonomy of social search reveals important similarities and differences between many social search systems. This analysis reveals a gap in social search approaches. A practical method was identified that allows users to directly leverage social search without special features built into search engines. Research limitations/implications For feasibility reasons, Google was used as an example of a search system that can be used for carrying out social searches. Practical implications The paper provides several practical implications for web searchers as well as web designers. In particular, some recommendations are provided for the design of search engines, digital libraries and browser add‐ons. Social implications The study demonstrates the value and power of “collective intelligence” in web search. It shows how general web searches can be enhanced through using socially enhanced web‐based tools such as social bookmarking systems, social tagging services and social media sites. Originality/value This is the first study that provides a granular analysis of the notion of social search and puts forward a taxonomy of social search. The use cases developed and reported are created based on real search topics, and show the value and validity of the approach taken.
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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.126 | 0.059 |
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".