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Rant - On YouTube Censorship and how to fucking stop it

Author: A Philosophical Mind

An unscripted rant on YouTube Censorship and how to fucking stop it born out of the endless trope that YouTube somehow being a private company absolves them from breaking the law...

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On how NOT to structure an argument - Contextual biases

Author: A Philosophical Mind

A thesis on the contextual biases

A bias is a preference or outlook to give or hold a different perspective, often accompanied by a refusal to consider the possible merits of alternative points of view.

All the biases, are something we ALL have (yes even me) :) - it has been evolutionary hardwired into us as a sort of shortcut to decision making when speed was more important than accuracy...

For instance - tending to see advocacy in your surroundings even when there are none:
When you lived on the savanna and could be eaten by a lion at any moment - the people who thought 'LION!!!' every time a twig broke or a shadow moved (even when there was no lion) were the ones who survived in the long run....

So, they are not bad as such... but in cases where accuracy (i.e. the truth) matters, then we need to be aware of them, so they can be countered and we can get it right, when we need to.

Note: No bias stand alone, but are all part of a larger network of biases either working with or against each other.

When it comes to biases, they are usually divided into 5 categories:
1. Cognitive biases
2. Conflicts of interest
3. Prejudices
4. Statistical biases
5. Contextual biases

In this blog, we will look at Contextual biases.

Contextual biases:
when we talk about contextual biases there are generally considered to be 9 contextual biases:
1. Inductive bias
2. Experimenter bias
3. Educational bias
4. Academic bias
5. Full text on net bias
6. Publication bias
7. Reporting bias
8. Media bias
9. Social desirability bias

Inductive bias:
The inductive bias (or learning bias) of a learning algorithm is the set of assumptions that the learner uses to predict outputs given inputs that it has not encountered.

Example:
An example of an inductive bias is Occam's razor, where you assume that the simplest consistent hypothesis about the target function is the best.
Here consistent means that the hypothesis of the learner yields correct outputs for all the examples that have been given to the algorithm.

Experimenter bias:
Experimenter bias (or observer-expectancy effect, experimenter-expectancy effect, expectancy bias, observer effect) is a form of reactivity in which a researcher's cognitive bias causes them to subconsciously influence the participants of an experiment.

Example:
An example of the observer-expectancy effect is demonstrated in music back masking, in which hidden verbal messages are said to be audible when a recording is played backwards.
Some people expect to hear hidden messages when reversing songs, and therefore hear the messages, but to others it sounds like nothing more than random sounds.
Often when a song is played backwards, a listener will fail to notice the hidden lyrics until they are explicitly pointed out, after which they are obvious.

Educational bias:
Educational bias (or bias in education) refers to real or perceived bias in the educational system.

Example:
The content of school textbooks is often the issue of debate, as their target audience is young people, and the term "whitewashing" is used to refer to selective removal of critical or damaging evidence or comment, for instance in religious bias in textbooks that is observed in countries where religion plays a dominant role.

Academic bias:
Academic bias is the bias or perceived bias of scholars allowing their beliefs to shape their research and the scientific community.

Example:
Some research supports the possibility of an academic bias against political conservatives and the highly religious.
An audit study suggests that entrance into a clinical psychology graduate program is negatively affected by whether the applicant is a conservative Protestant.
Examination of the comments made by members of the admission committees of medical schools also indicated religious candidates were more closely questioned because of their beliefs.
Other research indicates a willingness of academics to openly admit that they are less likely to hire a colleague, if they find out that the colleague is either religiously or politically conservative.

Full text on net bias:
Full text on net bias is the tendency of scholars to cite academic journals with open access, i.e. journals that make their full text available on the Internet without charge, over toll-access publications.

Example:
One study concluded that authors in medical fields concentrate on research published in journals that are available as full text on the internet, and ignore relevant studies that are not available in full text, thus introducing an element of bias into their search result.

Publication bias:
Publication bias is a type of bias that occurs in published academic research. when the outcome of an experiment or research study influences the decision whether to publish or otherwise distribute it.

Example:
The presence of publication bias in the literature has been most extensively studied in biomedical research.
Investigators following clinical trials from the submission of their protocols to ethics committees (or regulatory authorities) until the publication of their results observed that those with positive results are more likely to be published.
In addition, studies often fail to report negative results when published, as demonstrated by research comparing study protocols with published articles.

Reporting bias:
Reporting bias is when you choose to selectively reveal or suppress information about subjects in your study (past medical history, smoking, sexual experiences, etc.).

Example:
Litigation brought upon by consumers and health insurers against Pfizer for the fraudulent sales practices in marketing of the drug gabapentin in 2004 revealed a comprehensive publication strategy that employed elements of reporting bias.
Spin was used to put emphasis on favorable findings that favored gabapentin, and to explain away unfavorable findings towards the drug.
In this case, favorable secondary outcomes became the focus over the original primary outcome, which was unfavorable.

Media bias:
Media bias is the bias or perceived bias of journalists and news producers within the mass media in the selection of events and stories that are reported and how they are covered.

Example:
Mark Halperin (ABC) stated in an internal e-mail message during the 2004 US Election that reporters should not artificially hold George W. Bush and John Kerry equally accountable to the public interest, and that complaints from Bush supporters were an attempt to get away with renewed efforts to win the election by destroying Senator Kerry.

Social desirability bias:
Social desirability bias is a social science research term that describes a type of response bias that is the tendency of survey respondents to answer questions in a manner that will be viewed favorably by others.
It can take the form of over-reporting good behavior, or under-reporting bad, or undesirable behavior.

Example:
Because of the toxic atmosphere surrounding Trump in the 2016 US Election, most research polls done before the election showed Hillary with as much as a 98% chance of winning, because most Trump voters, due to undue social pressure, under-reported set undesirable vote.

Good now you know what your (and my) flaws are, so now you can identify them in others and avoid them in yourself :)

Now go forth and find the truth...

A Philosophical Mind
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