3 ms·

Your logic is inconsistent. This author is applying real data to an assertion. That assertion is backed up not only by my own experiences, but to the dozens i

Your logic is inconsistent. This author is applying real data to an assertion. That assertion is backed up not only by my own experiences, but to the dozens if not hundreds or thousands of women sharing this all over social media with something akin to "see, this happens to all of us." The author makes no such blanket statement as you suggest--rather she shows the results of her own study. What's facile is not her conclusions but rather the non data backed assertions you claim. You are the one asserting she extends her findings beyond her own sphere. to quote the author: "I only have the data I have. I don’t know whether women were simply more willing to submit reviews that include critical language, or whether men removed language from their review documents before submitting. But the directional indication is striking and calls for further investigation by managers and HR departments. At most mid-size or large tech companies, HR leaders supervise review scores to uncover and correct patterns of systematic bias. This is a call to action to bring the same rigor to the review language itself."

cperciva 12y ago
Maybe I'm misinterpreting what the author wrote, but it seems clear to me that, having identified a statistical anomaly, she is assuming one explanation -- that there is a problem with the review process -- and ignoring other potential explanations (e.g., a bias in hiring which results in non-aggressive women never being hired and thus never being reviewed).
- melindajb 12y ago
  I do think you are misinterpreting the author. 1. She states very clearly in the last line: this is a call to action to review the language in reviews for for bias, not just the scores. 2. I can see you and others here very willing to accept the possibility that women just are more aggressive and that worries me about potential bias far more than this study does. Do you see that in yourself? To be fair to you, I see in myself the bias to believe what I read here because it is absolutely consistent with my experience. If you do too (i.e. you believe women in tech/business are somehow more aggressive, out of gender norm, etc) then that is a perfect reason to step back for a minute and examine your perceptions. 3. As you state above, non aggressive women never getting hired--one possible reason could be, as others have stated is that non aggressive women do not have the emotional or financial resources to withstand the cultural assault of doing something outside of gender norms. Do you see why that's a problem? Why the stereotypes of behavior in tech/business that are predominantly male are so damaging? And why it would be so important to look at the language used here? 4. Honestly, now, are you not struck by the difference in tone in those reviews? Or do you truly believe the women's words were "deserved?" because that's the fundamental question that the author asks us to consider.
  - cperciva 12y ago
    1. She states very clearly in the last line: this is a call to action to review the language in reviews for for bias, not just the scores. Right, and I'm taking that as saying "HR should look for bias in the language used in reviews because I think they will find it". 2. I can see you and others here very willing to accept the possibility that women just are more aggressive and that worries me about potential bias far more than this study does. Do you see that in yourself? Do I think that women in general are more aggressive than men? No. Do I think that women in tech fields are more aggressive than men? I don't have enough evidence to form an opinion about this, but given the challenges which women must overcome in order to succeed in tech fields, I think it's entirely possible that the less-aggressive women tend to get filtered out. 3. As you state above, non aggressive women never getting hired--one possible reason could be, as others have stated is that non aggressive women do not have the emotional or financial resources to withstand the cultural assault of doing something outside of gender norms. Do you see why that's a problem? Why the stereotypes of behavior in tech/business that are predominantly male are so damaging? And why it would be so important to look at the language used here? Absolutely. I'm not willing to jump to the conclusion that the reviews are wrong, though. are you not struck by the difference in tone in those reviews? Or do you truly believe the women's words were "deserved?" Given that I do not know the men or women in question, I am unable to form an opinion about whether the adjectives used to describe members of either gender were accurate.
    - melindajb 12y ago
      I can see where you are coming from. I understand that innate perception, no matter how well intentioned, is a really hard thing to observe and think critically about in ourselves. I just want to ask you about the last line. you truly, truly have NO opinion about what you read there? you don't think there's anything amiss or disturbing at all about the apparent pattern? I confess based on that and your answer to #3, you sound a bit, well like a robot--someone so deeply embedded in your own logic that you cannot see the larger picture. I don't think you're a hopeless misogynist at all, but I do think you might be missing something.
      - 12y ago
- lmkg 12y ago
  > having identified a statistical anomaly, she is assuming one explanation That is where you are going askew. There are two ways to derive knowledge from data. A) Start with a theory. Generate falsifiable hypothesis. Perform observation that validates or contradicts hypothesis. B) Start with statistical anomaly. Generate hypothesis (or hypotheses). Validate with more data. Your objection assumes that the author took path B. She did not, she took path A: Starting with the hypothesis, and looking for confirming or disconfirming data. The fact that there are other possible explanations is a valid point, but what you are doing is starting down path B, with your starting point being half-way through someone else's path A. It's a valid point, but it's not really an objection. In this approach, it would be a next-step, not something expected at this stage. Sidenote: Most programmer-types seem to implicitly assume path B when reading research results. I assume this is because path B is how data-mining and machine learning work. Historically, path A is the more common approach, and is closer to the definition of the scientific method. Path B has only opened up recently, since data-collection has become more ubiquitous.

cperciva 12y ago

Maybe I'm misinterpreting what the author wrote, but it seems clear to me that, having identified a statistical anomaly, she is assuming one explanation -- that there is a problem with the review process -- and ignoring other potential explanations (e.g., a bias in hiring which results in non-aggressive women never being hired and thus never being reviewed).

melindajb 12y ago
I do think you are misinterpreting the author. 1. She states very clearly in the last line: this is a call to action to review the language in reviews for for bias, not just the scores. 2. I can see you and others here very willing to accept the possibility that women just are more aggressive and that worries me about potential bias far more than this study does. Do you see that in yourself? To be fair to you, I see in myself the bias to believe what I read here because it is absolutely consistent with my experience. If you do too (i.e. you believe women in tech/business are somehow more aggressive, out of gender norm, etc) then that is a perfect reason to step back for a minute and examine your perceptions. 3. As you state above, non aggressive women never getting hired--one possible reason could be, as others have stated is that non aggressive women do not have the emotional or financial resources to withstand the cultural assault of doing something outside of gender norms. Do you see why that's a problem? Why the stereotypes of behavior in tech/business that are predominantly male are so damaging? And why it would be so important to look at the language used here? 4. Honestly, now, are you not struck by the difference in tone in those reviews? Or do you truly believe the women's words were "deserved?" because that's the fundamental question that the author asks us to consider.
- cperciva 12y ago
  1. She states very clearly in the last line: this is a call to action to review the language in reviews for for bias, not just the scores. Right, and I'm taking that as saying "HR should look for bias in the language used in reviews because I think they will find it". 2. I can see you and others here very willing to accept the possibility that women just are more aggressive and that worries me about potential bias far more than this study does. Do you see that in yourself? Do I think that women in general are more aggressive than men? No. Do I think that women in tech fields are more aggressive than men? I don't have enough evidence to form an opinion about this, but given the challenges which women must overcome in order to succeed in tech fields, I think it's entirely possible that the less-aggressive women tend to get filtered out. 3. As you state above, non aggressive women never getting hired--one possible reason could be, as others have stated is that non aggressive women do not have the emotional or financial resources to withstand the cultural assault of doing something outside of gender norms. Do you see why that's a problem? Why the stereotypes of behavior in tech/business that are predominantly male are so damaging? And why it would be so important to look at the language used here? Absolutely. I'm not willing to jump to the conclusion that the reviews are wrong, though. are you not struck by the difference in tone in those reviews? Or do you truly believe the women's words were "deserved?" Given that I do not know the men or women in question, I am unable to form an opinion about whether the adjectives used to describe members of either gender were accurate.
  - melindajb 12y ago
    I can see where you are coming from. I understand that innate perception, no matter how well intentioned, is a really hard thing to observe and think critically about in ourselves. I just want to ask you about the last line. you truly, truly have NO opinion about what you read there? you don't think there's anything amiss or disturbing at all about the apparent pattern? I confess based on that and your answer to #3, you sound a bit, well like a robot--someone so deeply embedded in your own logic that you cannot see the larger picture. I don't think you're a hopeless misogynist at all, but I do think you might be missing something.
    - 12y ago
lmkg 12y ago
> having identified a statistical anomaly, she is assuming one explanation That is where you are going askew. There are two ways to derive knowledge from data. A) Start with a theory. Generate falsifiable hypothesis. Perform observation that validates or contradicts hypothesis. B) Start with statistical anomaly. Generate hypothesis (or hypotheses). Validate with more data. Your objection assumes that the author took path B. She did not, she took path A: Starting with the hypothesis, and looking for confirming or disconfirming data. The fact that there are other possible explanations is a valid point, but what you are doing is starting down path B, with your starting point being half-way through someone else's path A. It's a valid point, but it's not really an objection. In this approach, it would be a next-step, not something expected at this stage. Sidenote: Most programmer-types seem to implicitly assume path B when reading research results. I assume this is because path B is how data-mining and machine learning work. Historically, path A is the more common approach, and is closer to the definition of the scientific method. Path B has only opened up recently, since data-collection has become more ubiquitous.