Acknowledging contributions to online expert assistance
Bibliographic record
Abstract
We present a poster which contains a sequence of a question, answers to this question and comments regarding acknowledging content on BioStar. Biostar.stackexchange.com is a website where questions about Bioinformatics can be asked and answered. Users can also comment on both the questions and the answers. The site is modelled after www.stackoverflow.com (see description from Joel Spolsky), a comparable site for programmers.
 
Users find the site valuable both for answers to questions they have and as a reference. Since the content can also be viewed without registration the site likely reaches a larger audience. For instance, BioStar questions are often referenced on Twitter and FriendFeed. This leads to the question of how contributions to such a site can be measured and how they should be cited on other websites. The site itself has some mechanisms in place, which are mainly meant to encourage users; it uses reputation points and so called badges to recognize the quality of contributions. Reputation points are given by the community, who can up- or down- vote questions and answers. Badges are automatically awarded based on predefined criteria. Users with higher reputation levels can also manage the site itself, for instance by adding tags, editing questions and answers or even closing and deleting them. The reputation mechanism is interesting since it is not automatically given based on input provided but actually decided on by fellow users based on their judgement of the quality.
 
We have used the BioStar website itself to ask “How do you acknowledge Biostar and its contributors in your research output?" (http://biostar.stackexchange.com/questions/6062/) 
Currently (April 2011) this question is still active and in the top-10 of questions with most votes, indicating clear interest by the community for ways to acknowledge content from BioStar. The poster gives some interesting viewpoints on the matter. Some examples indicate how useful BioStar was in practical cases, for instance by showing how multiple consequences from gene variations can be mined, results of which could immediately be applied to real research questions. Of course people wanted to acknowledge BioStar in such cases, and indicated how they did that in practice. Although a paper about BioStar itself was suggested as a useful reference and way to advertise the site, people seem to agree that this is not the best way to acknowledge individual contributions. As an alternative, an example of a citation standard for blogs developed by the National library of medicine is mentioned, which also keeps track of the date (and thus version) of the cited document. The use of the Document Object Identifier was discussed, as a way to get easy links to fixed versions of a question with answers. Although the answers provided are given in the context of the BioStar community, the presented content is applicable to other online resources as well and could provide valid input to other communities.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".