OpenLLM-France/wikimedia huggingface dataset:
# Should Wikipedia users archive their talk pages?

By archiving talk pages we mean creating separate archive pages rather than leaving the past conversations in the page revision history.

## Wikipedia users should archive their talk pages
### Pro

* Pro Failure to archive is disrespectful to those who formulated their ideas in a discussion on the user talk page. It removes not only the copyrighted creations of the user in question but also the copyrighted creations of other discussion participants.
* Pro Failure to archive makes it difficult for others to see what has already been discussed with the user, which makes administration more difficult.
* Pro Failure to archive makes it more difficult to detect problematic users. Since, problematic users receive complaints on their talk pages.

### Con

* Con Users should have right to privacy, right to be forgotten to some extent.
** Objection That is a very limited implementation of that right since what the user wrote in public discussion fora stays anyway.
*** Objection There is still a considerable effect since anyone can quickly see what is on the user talk page, but it is much harder to find all the potentially problematic posts of the user in the discussion fora. And as long as users are often talked to in order to regulate their behavior, the user talk page has a much higher tendency to act as a transgression record than e.g. a discussion contribution history.
* Con Users should have the right to remove messages that they find problematic, which contradicts proper archiving.
** Objection One can combine removing problematic messages with proper archiving. Such a combination still stands in contrast with quasi-archiving into revision history via deletion.
# Should Wikipedia and other wiki user signatures be required to be unadorned default?

Wikipedia (and other similar wikis) allows users to customize their signatures, including changes of font face and color, background color and choice of any text in the signature including non-alphanumerical Unicode code points, including smileys. Should that be allowed? Or should it rather be mandatory to use the default signature?

## Wikipedia and other wiki user signatures should be required to be unadorned default
### Arguments for

* Pro Too many users tend to create unseemly looking signatures. They choose garish colors, fonts, put bizarre labels on the links to their talk page, etc. This could be addressed by creating restricting guidelines, but the language of beauty is hard to make objective and prohibiting signature customization entirely is the one policy that is very easy to administer.
** Objection Other policies are very easy to administer, e.g. disallowed are changes in font face, font color, and background color.
*** Objection That has to be administered/enforced, whereas customization of user signatures can probably be disabled technically.
* Pro The freely customizable signatures enable something like an arms race for whose signature is going to stand out more, resulting in some truly bad signatures.

### Arguments against

* Con Users should be allowed some room for self-expression. While it is true that some user signatures are very bad, this can be addressed by a guideline.
** Comment As argument for stated, the guidelines can be hard to formulate in a clear objective language.
** Objection Review of various polls and votes shows that most users do fine with default signature, without this form of self-expression.
** Objection Users have their user page for self-expression. Their user page is not copied to so many places, unlike their signature.
# Should Wikipedia essays be moved out of Wikipedia namespace?

Wikipedia has pages in Wikipedia namespace that are classified as essays, e.g. W: Wikipedia:Don't bludgeon the process and W: Wikipedia:Civil POV pushing. These are neither policies nor guidelines. The banner says that "it has not been thoroughly vetted by the community", per W: Template:Essays and W: Template:Supplement. Despite the unknown consensus support, they are often referenced from discussions. They even have shortcut redirects to ease referencing, e.g. W: Wikipedia:BLUDGEON and W: Wikipedia:SEALION.
Should these essay pages be moved away from Wikipedia namespace, e.g. to user space?

## Wikipedia essays should be moved out of Wikipedia namespace
### Arguments for

* Pro By being referenced from discussions as if they had policy force and applied as if they did so, they are de facto policies to some extent. And yet, they mislead the reader into thinking they are not, and that it is not known whether they have consensual support. They create an unpleasant environment with a taste of dishonesty, in which experienced users know which of the essays have the force of policy and which do not. This runs counter to the requirement of openness of the project and fairness of administration.
* Pro Expanding on the above, some of the essays contain clearly untrue statements, which creates the impression that they are not taken seriously. To wit, W:WP:BLUDGEON states "The more often you express the same ideas in a discussion, the less persuasive you become.", clearly untrue. And yet, BLUDGEON is being invoked as a policy, and has convenient shortcuts WP:BLUD and WP:BLUDGEON to ease such referencing. BLUDGEON is templatized as "supplement".
* Pro There are over 2000 pages in the English Wikipedia classified as essays. No one can be reasonably expected to read them, and then try to figure out whether they are supported by consensus.

### Arguments against

* Con Regulating everything only via formal policies and guidelines would be overly bureucratic.
** Objection If there is a consensus that a particular text should become a guideline--an overridable policy or a weakly enforced policy--it should be easy to get it to a request for comment and have it formally approved. Then, the page would state it is a guideline, and it would not mislead anyone by stating its consensus status is not known.
** Objection The application of a page expressly marked as having an unknown consensus status as a policy seems to be very problematic, and this concern overrides the need to have informally created artifacts.

## Exernal links

* Wikipedia:Category:Wikipedia essays -- has over 2,100 pages and 47 subcategories
# Should Wiktionary administrators be removable by one third superminority?

The English Wiktionary administrators are not removable one by third superminority; plain 50% majority is required. Should they be so removable?

## Wiktionary administrators should be removable by one third superminority
### Arguments for

* Pro It would mean that a person can continue being an administrator only if there is a consensus for that, for some value of "consensus". It would greatly increase accountability, which is currently missing probably in part since administrator groups tend to behave like cliques or priestly classes, and protect their group or class interests, showing group solidarity. Experience shows overt misconduct including out of process deletions and untrue reasons for deletions as well as overt incivility by administrators go unpunished and the administrators guilty of these obtain remarkable support.

### Arguments against

* Con It would make administrators too vulnerable by problematic editors and malcontents, and reduce administrator ability to effectively limit harmful conduct for the fear of making too many enemies by blocking them.
** Objection A policy similar to the proposed one is implemented in Czech Wikipedia (since 2007) and Czech Wiktionary (since 2016), and it has proved to work reasonably well. The above is a mere speculation not based on any empirical evidence.
*** Objection It is not obvious that it has proved to work reasonably well without having a careful look at how these projects operate and whether it caused actual problems. In Czech Wiktionary, the policy lead to a great reduction of administrator force, and one would have to carefully verify that the loss was a result of bad administration rather than opposition by disgruntled problematic malcontents.
**** Objection Fair enough as for Czech Wiktionary; one has to look closely and if one is not a Czech native speaker, one may find it hard to make a proper determination. However, no such thing happened in Czech Wikipedia, which is a much larger project in terms of editors, and the English Wiktionary is much larger than the Czech Wiktionary in those terms.
* Con 50% is very easy to reach.
** Objection This is not borne out by experience in the English Wiktionary. In votes attempting to desysop administrators for misconduct, none of the votes reached 50%; most have not reached even 1/3 for desysopping.
# Russian/Introduction/Numbers
### Numbers

0 ноль [nǔl']
1 один [ǔdǐn]
2 два [dva]
3 три [tri]
4 четыре [chetyre]
5 пять [pyat']
6 шесть [shest']
7 семь [sem']
8 восемь [vosem']
9 девять [devät']
10 десять [dyesit']
11 одиннадцать [adinatsat]
12 двенадцать [dvinatsat]
13 тринадцать [trinatsat]
14 четырнадцать [chityrnatsat]
15 пятнадцать [pitnatsat]
16 шестнадцать [shistnatsat]
17 семнадцать [simnastat]
18 восемнадцать [vocemnatsat]
19 девятнадцать [divitnatsat]
20 двадцать [dvatsat']
21 двадцать один [dvatsat ǔdǐn]
22 двадцать два [dvatsat dva]
30 тридцать [tritsat']
40 сорок [sorak]
50 пятьдесят [pidisyat]
60 шестьдесят [shizdisyat]
70 семьдесят [syem'disit]
80 восемьдесят [vosim'disit]
90 девяносто [divinosta]
100 сто [sto]
110 сто десять [sto dyesit]
200 двести [dvyesti]
300 триста [trista]
400 четыреста [chityrista]
500 пятьсот [pit'sot]
600 шестьсот [shist'sot]
700 семьсот [sim'sot]
800 восемьсот [vasim'sot]
900 девятьсот [divit'sot]
1000 тысяча [tysicha]
1000000 миллион
1000000000 миллиард
# Russian

Russian refers to anything related to Russia, including people, language, and culture.

## Resources

Special:Prefixindex/Russian/

## Recommended Reading

* Schaum's Outline of Russian Grammar
* Barron's 501 Russian Verbs
* The New Penguin Russian Course

## See Also

* Wikipedia: Russian
# Russian/Introduction/Letters the same as in the Latin alphabet

Russian alphabet has 33 letters, out of which 10 are vowels and 21 are consonants. Two of the letters (Ь and Ъ) are used for changing sound of the preceding consonant. Note, that in the list below first is capital, then small letter of cyrillic alphabet, then comes explanation of how the sound should sound/feel like when pronounced
The written forms of many of the cyrillic letter appear significantly different from their printed counterparts. The written forms, however, will be discussed in a later lesson.

## Latin letters

Of the 33 letters in the Cyrillic alphabet, 5 of them look and sound very similar to their latin counterparts. There are subtle differences in pronunciation, but these will be addressed in the intermediate Russian course. These letters are:
А, а like in wand
К, к like in kitchen
М, м like in mammal
Т, т like in tail
О, о
This letter can make either the sound oh like in bold, or ah like in hot. It makes the sound oh when it is the accented vowel, and ah when unaccented. We will talk more about what accented or unaccented means in the lesson on accents.

## Vocabulary

With the limited letters you know, you there are few useful words that can be formed. The one vocabulary word, therefore is:
Кот - Tomcat
Course:Introduction to Russian
# Russian/Introduction/Old

Welcome to the Introduction to Russian course.

## Syllabus

This course will discuss the basics of the Russian language. It will begin with the alphabet and basic pronunciation and continue through simple noun declension and verb conjugation, with exercises along the way. It will focus on a concurrent devlopment of vocabulary and grammatical understanding. Eventually, this course will be followed by an Intermediate Russian course.

## Lectures
### Basics

* Russian Alphabet
* Accents (Ударе'ние)
* Greetings
* Numbers
* Essential phrases
* Cursive writing
# Russian/Introduction/Essential phrases
### Essential phrases

    Как тебя зовут? [kak tebya zovut] = What's your name?
    Как вас зовут? [kak vas' zovut] = What's your name? (Formal, preferred if you don't know the person.)
    Меня зовут ... [menya zovut] = My name is ...
    Сколько тебе лет? [skol'ko tebye let] = How old are you?
    Мне ... (лет) [mne] = I'm ... (years old)
    Откуда ты родом? [atkuda ty rodom] = Where are you from?
    Я из... [ya iz] = I'm from ...
    Канады [canady] = Canada
    Англии [anglii] = England
    Финляндии [finländii] = Finland
    Франции [francii] = France
    Германии [germanii] = Germany
    Италии [italii] = Italy
    Испании [ispanii] = Spain
    США [se-sha-a]= USA
    Бразилии [brazilii] = Brazil
    Ямайки [yamaiki] = Jamaica
    Бельгии [belgii] = Belgium
# Russian/Introduction/Alphabet

This page is almost identical to Russian/Alphabet which seems sightly more developed. Page before blanking: permanent link
# Survey research and design in psychology/Tutorials/Qualitative analysis

* This tutorial contains notes and suggestions which aim to develop skills in qualitative analysis (particularly thematic analysis and multiple response analysis) for the lab report. Before tackling this tutorial, make sure you are familiar with Qualitative research) (lecture).

## Contents

* Overview
* Thematic analysis
* Multiple response analysis

## Recordings

* Screencasts
# Survey research and design in psychology/Tutorials/Psychometrics/Instructor notes

Instructor notes

## Overview

This tutorial is designed as a 110 minute, facilitated, computer lab session. In such a session, the following can be achieved:

### Factor analysis (70 mins)

* Steps: Overview key points about the steps involved in factor analysis.
* Exercise 1: Demonstrate and guide students step-by-step through Exercise 1, including:
** The SPSS EFA dialog box options and main choices to be made.
** Explain each of the key output tables and how they can be interpreted.
*** Learning to interpret SPSS EFA output is a bit like learning how to drive; when you start driving with a L plate you feel overwhelmed by all the information coming in, but then as you become more expert, you learn what to pay attention to and what can be ignored.
** This is a very simple EFA, with a small number of variables and factors and a simple structure, so its excellent for b beginners
* Exercise 2: Guide students step-by-step through Exercise 2 - as per Exercise 1. This is a more advanced exercise because it involves comparing a two and a three factor solution, then removing two items, and re-running.
* Exercise 3: Introduce and explain the data set (11 items about Managerial Style/Skills - what is the underlying factor structure?), then ask participants to then conduct an EFA (PAF, Oblimin) on their own, to help reinforce what has been taught in the first two exercises. Allow 5-10 min to complete on their own, then demonstrate and go through the analysis on the board.

### Internal reliability (10 mins)

* Exercise 1: Guide students step-by-step through Exercise 1.
* Exercises 2 and 3 can be completed on their own, outside of class time if further practice is desired.

### Composite scores (10 mins)

* Exercise 1: Guide participants through creating a composite score for Openness (which also involves recoding responses to the negatively worded items) and obtaining descriptive statistics and a histogram
* Exercises 2 and 3 (creating composite scores for Student Satisfaction and Maths and English Teaching) can be completed on their own, outside of class time if further practice is desired.

### Lab report 3 (5-10 mins)

* Discuss the requirements for Lab report 3 and allow Q&A
* If the tutor if unsure about the answers, encourage the students to ask on the Moodle discussion forums
# Survey research and design in psychology/Tutorials/Psychometrics/Internal consistency

This tutorial involves learning how to examine internal consistency using SPSS's reliability function.

## Exercises

Exercise 1: Openness
View the accompanying screencast:
Data file: data_15_1.sav
Allen and Bennett 15.2, pp. 205-213
Rating scale
1
Very inaccurate
2
Moderately inaccurate
3
Neither inaccurate or accurate
4
Moderately accurate
5
Very accurate
Items
1
Believe in the importance of art.
2
Tend to vote for conservative political candidates
3
Have a vivid imagination
4
Avoid philosophical discussions
5
Tend to vote for liberal political candidates.
6
Do not like art.
7
Carry the conversation to a higher level.
8
Am not interested in abstract ideas.
9
Enjoy hearing new ideas.
10
Do not enjoy museums.
Syntax
* Openness reliability analysis.
* Recoding.
compute Open2r=6-Open2.
compute Open4r=6-Open4.
compute Open6r=6-Open6.
compute Open8r=6-Open8.
compute Open10r=6-Open10.
EXECUTE.
variable labels
Open2r 'O2. Tend to vote for conservative political candidates.'
Open4r 'O4. Avoid philosophical discussions.'
Open6r 'O6. Do not like art.'
Open8r 'O8. Am not interested in abstract ideas.'
Open10r 'O10. Do not enjoy going to art museums.'.
* Reliability.
RELIABILITY
/VARIABLES=Open1 Open2r Open3 Open4r Open5 Open6r Open7 Open8r Open9 Open10r
/SCALE('ALL VARIABLES') ALL
/MODEL=ALPHA
/STATISTICS=CORR
/SUMMARY=TOTAL.
RELIABILITY
/VARIABLES=Open1 Open2r Open3 Open4r Open6r Open7 Open8r Open9 Open10r
/SCALE('ALL VARIABLES') ALL
/MODEL=ALPHA
/STATISTICS=CORR
/SUMMARY=TOTAL.
Solution
10 items; reverse-code 2, 4, 6, 8, 10
α = .77, N = 60
drop #5, which gives α = .84 :
# Survey research and design in psychology/Tutorials/Qualitative analysis/Overview

Overview
View the accompanying screencast:
Qualitative data generally consists of words (although it could consist of any non-numerical data). There are two basic approaches to qualitative analysis:
Qualitatively (as words and themes/meaning) or
Quantitatively (code as categories and analyse frequencies and percentages).
The aim of the qualitative analysis for the lab report is to analyse qualitative data (for probably the first time) and to present the main underlying themes or meaning for university student satisfaction and/or dissatisfaction.
The qualitative analysis results, as well being of potential use in and of themselves, can also serve as a validity check for other analyses e.g., are the themes consistent with a factor analysis of the quantitative satisfaction items?
Use either a research question or a hypothesis to guide the qualitative analysis, e.g., "What are the main areas of university student dissatisfaction?". :
# Survey research and design in psychology/Tutorials/Qualitative analysis/Thematic analysis

* Qualitative
* In a thematic analysis approach:
* Immerse yourself in the data: Read carefully over the responses to one of the open-ended questions (e.g., most or least satisfying aspects of university) - and probably to read through these responses several times.
* Themes: Look for underlying themes, factors, meanings etc.
* Visualisation: You may find it helpful to draw a diagram to depict the key themes and their inter-relationships
* Thick description: Then attempt to describe what you interpret to be the key meanings expressed by participants. This may involve describing a series of themes, but it might also include how those themes connect to one another. Offer clear definitions of key constructs which you think characterise participant responses and illustrate these where appropriate with direct quotes.
# Survey research and design in psychology/Tutorials/Recordings/Virtual tutorials

Recordings of virtual tutorials are available:
* 2018 links will be added to each of the tutorial pages as they occur
* 2017 links are available on each of the tutorial pages
# Survey research and design in psychology/Tutorials/Qualitative analysis/Multiple response analysis

Multiple response analysis
View the accompanying screencast:
In this content analysis approach, qualitative data is recoded as numeric numeric data which indicate categories of themes which are then summarised statistically and graphically:
Immerse yourself in the data and create a coding sheet:
Read over the responses
Make a list of the main themes and allocate a number to each e.g.,
1 = Parking,
2 = Teaching quality,
3 = Computing facilities etc.
Consider whether to group some of the themes into a broader category - if in doubt, keep as separate themes because there will still be a chance to recode them if necessary later on (e.g., after looking at the results)
Themes/Coding:
Insert new variables in your data file - one for each of the multiple response options (e.g. participants could list up to two areas of satisfaction, so add two variables e.g., satis-response1 and satis-response2).
Enter category codes for each case's comments. If they made multiple comments, then code one response per column, up to the max number of possible responses.
If there was less than the maximum number of responses, then leave those cells as missing data (no code.
You may also need a code for "Other": In doing your coding, you may find responses that don't seem to fit into the main themes you identified. These could be coded as an extra category called something like "Other"
Inter-rater reliability:
It could be a good idea to do the coding in conjunction with someone else
Ideally, create the coding sheet together, then code the data independently, then go over the codes for each response together and discuss any codings you disagreed on. This is the basic way of inter-rater reliability.
Multiple response analysis
Its a good idea to add value labels (it will make the output easier to interpet).
Perform multiple response analysis (download some of the examples from this page about how to do this).
Note that you can also analyse the multiple response frequencies by an independent variable (e.g., gender) using Multiple Response - Crosstabs.
Examine the frequency and percentage of responses and the percentage of cases - present these summary statistics as a table in your results and possibly also as a bar graph
In writing up the results, don't lose the richness of the qualitative data; try to provide a rich description of the themes to accompany the statistical summaries. :
# Survey research and design in psychology/Tutorials/Recordings/Screencasts

Screencasts (screen recordings with audio) are available for each of the tutorial exercises.
Each screencast is a maximum of 5 minutes. There are several screencasts for each tutorial.
* View the accompanying screencast:
There are two ways to access the screencasts:
* Via tutorial pages - links to screencasts are provided in green boxes, e.g. like the one on the right:
* Via youtube playlist - lists all the screencasts